A Bibliography of Publications of Thomas Ulrich Zeugmann

Thomas Ulrich Zeugmann

Associate Professor

Research Institute of Fundamental Information Science

Fukuoka 812 Japan

Tel: ?n/a? FAX: ?n/a?

E-mail: thomas@rifis.kyushu-u.ac.jp (Internet)

02 February 2019 Version 1.17

Abstract

This bibliography records publications of Thomas Ulrich Zeugmann.

Title word cross-reference

& [Zeu92a]. δ [AZ08]. k [RZ98e, RZ98g]. Z/p^{α} [Zeu89b].

-delay [AZ08]. -Variable [RZ98e, RZ98g].

10th [EFW93]. 16th [MT99]. 1st [DJS90, DJS91].

24th [JMSZ16]. 2nd [BJS93, Vit95, Zeu89a].

3rd [FC90].

4th [AJ94, HS98].

5th [AJ94, Hau92].

60th [CSZZ08]. 6th [JSZ95a, Pit93].

7th [AS96].

’89 [Jan89]. 8th [LM97, Yao95].

’93 [STA94]. 9th [RSWZ98a].

A-posteriori [Zeu83b]. ability [SZ97]. abstract [KZ89c]. ACM [FC90, Hau92, Pit93]. Across [ZL94, ZL95]. Active [FZ14]. AII [Jan89]. AII’92 [Jan92]. AII’94 [AJ94]. Akademgorodok [BBP96]. Algorithm [Zeu95, Zeu98]. Algorithmic [AJ94, AS96, JMSZ16, JL95, JSZ95a, LM97, RSWZ98a, Zeu97, BZ09, HSVZ13, BDCZ07, BSVZ14, CBRZ06, GTZ10, KSZ14]. algorithmischen [Zeu83a]. Algorithmisches [Zeu93b, Zeu93a]. Algorithms [RZ98a, Zeu89a, Zeu90b, Zeu92a, Zeu94a, Zeu94b, FZ14, RZ99b]. almost [KZ85]. ALT [GTZ10, JMSZ16, CBRZ06]. ALT’94 [AJ94]. ALT’95 [JSZ95a]. ALT’96 [AS96]. ALT’97 [LM97]. ALT’98 [RSWZ98a]. Ames [HS98]. amount [FZ11, JSZ12]. Analogical [AJ94, Jan87, Jan92, Jan89]. Analysis [RZ99a, RZ16, Zeu89a, Zeu94a, Zeu94b, Zeu95, Zeu98, RZ99c]. Analyzing [RZ98a, RZ99b]. Andrei [BBP96]. Annual [BM98, EFW93, FC90, Hau92, Pit93]. Anomalies [GKP^{+}95, GLZ00, KGZ^{+}90]. Approach [LZ92d]. approximations [LGZ05, SZ99, SZ02]. Artificial [Yao95]. Asking [ERS^{+}97a, ERS^{+}97c, ERS^{+}01]. Aspects [EFW93, MT99]. August [CDG89, Rov90]. Australia [AS96]. Australian [Yao95]. automata [FZP13, SZ97]. Average [ERS^{+}97a, RZ97, RZ98a, RZ98b, RZ99a, RZ00, RZ98e, RZ98f, Zeu94a, Zeu94b, Zeu95, Zeu98, ERS^{+}97c, ERS^{+}01, RZ98c, RZ98d, RZ99b, RZ99c, RZ98g]. Average-Case [RZ98a, RZ99a, RZ00, Zeu94a, Zeu94b, Zeu95, Zeu98, RZ99b, RZ99c].

Baase [Zeu89a]. Banská [Rov90]. Barcelona [Vit95]. Barzdin [Zeu87]. Based [JL95]. be [WZ92, WZ94a, WZ95b, Zeu03]. Behavior [RZ98a, RZ99b]. birthday [CSZZ08]. Boundaries [ZL94, ZL95]. Bounded [CJLZ97, CJLZ99, LZ93e, LZ93c, GJSZ18, LZ92a, LZ93f]. Bystrica [Rov90].

California [Pit93]. Can [WZ95b, Zeu03, WZ92]. Carlo [KZ89b]. Case [RZ98a, RZ99a, RZ00, Zeu94a, Zeu94b, Zeu95, Zeu98, RZ99b, RZ99c]. Castle [AJ94, Jan89]. Changes [LZ93e, LZ93c, LZ95c, BZ05, LZ92a, LZ93f, LZ95e]. Characterization [LZ92c, LZ94a]. Characterizations [Zeu83b, ZLK92, ZLK95]. Class [LZK92, ZLK92, JZ10a]. classes [JZ12, SZ02, Zeu84a]. Classification [WSZ94]. Classifying [SWZ97, WSZ93, WSZ95]. Clustering [IZZ10]. Co [FZ95, FZ96]. Co-Learning [FZ95]. coherent [AZ08]. Colloquium [HS98]. COLT [Zeu94c]. COLT’90 [FC90]. comparators [GJSZ18]. Complete [RZ99a, RZ99c]. complexity [FZP13, Zeu18]. compression [IZZ10]. Computation [RZ16, CDG89]. Computational [BM98, FC90, Hau92, Pit93, STA94, Vit95]. Computations [Zeu92b, Zeu89b]. Computer [EFW93, MT99, Rov90, Zeu89a]. Computing [Zeu90c]. Concept [CJLZ97, CJLZ99]. concepts [GLZ00, LGZ05]. Conference [BM98, BBP96, CDG89, JMSZ16, Pit93, RSWZ98a, Vit95, Yao95]. conjecture [Zeu87]. Conjunctive [RZ98a, RZ99b]. Consistency [WZ94b, WZ95a]. Consistent [AZ08]. Constraints [LZ94a]. correct [KZ85]. Cruz [Pit93]. Czechoslovakia [Rov90].

Data [CJLZ97, CJLZ99, FZ95, FZ96, LZ95a, LZ96b, RZ98e, RZ98f, TZ95a, WZ94a, IZZ10, JSZ12, LZZ08, RZ98g, TZ95b]. December [DJS91]. dedicated [JMSZ16, CSZZ08]. delay [AZ08]. Demands [LZ95c, LZ95e, LZ95d]. Dependence [LZ93b, LZ93a]. Design [Zeu89a]. deterministic [FZP13, SZ97]. developments [BZ09]. Dezember [BJS93]. distance [IZZ10]. done [Zeu03]. Driven [LZ94b, LZ96a]. Dual [ZLK92, LZK92, LZK96, ZLK95].

ed [Zeu89a]. Editor [JSZ95b, RSWZ98b, Zeu97, BSVZ14, GTZ10, KSZ14, Zeu18]. Editors’ [ACZ16, JMSZ16, HSVZ13]. effective [Zeu84b]. efficiency [LZ95d]. Efficient [ERS^{+}96, WZ95b]. Efficiently [ERS^{+}97a, RZ98e, RZ98f, WZ94a, ERS^{+}97c, ERS^{+}97b, ERS^{+}01, RZ98g, WZ92, Zeu03]. Eleventh [BM98]. Erasing [LWZ96a, LWZ96b, JKL^{+}00, LWZ96c]. Error [KZ91]. Ershov [BBP96]. Eurocolt [STA94]. EuroColt’95 [Vit95]. European [Vit95]. everywhere [KZ85]. Examples [ERS^{+}96]. expressible [JZ09, JZ10b]. extended [KZ89c].

Factors [Zeu92b]. families [LZZ08]. Fast [Zeu81, Zeu90c]. Fastest [Zeu82, Zeu83d, Zeu83c, Zeu84a]. FCT’89 [CDG89]. February [EFW93]. feedback [BZ06a, BZ11]. final [JL95]. Finite [RZ98e, RZ98f, Zeu82, RZ98g, SZ97, Zeu83c, Zeu06a]. first [JZ08, JZ10b, JZ12]. first-order [JZ08, JZ10b, JZ12]. Foreword [BDCZ07, CSZZ08, CBRZ06, Zeu18, ACZ16, BSVZ14, GTZ10, HSVZ13, JMSZ16]. formal [JSZ12]. formulae [JZ12]. Forty [CSZZ08]. foundations [Rov90]. four [JZ10b]. Freivalds [Zeu16]. Frequency [KZ89b, KZ91, FZP13]. Fukuoka [JSZ95a]. Functions [Zeu83b, FZ11, FZ14, JKL^{+}00, JKWZ01, JKWZ03, SZ99, SZ02, Zeu84a, ZZ08]. Fundamentals [CDG89]. Funktionen [Zeu93a, Zeu93b].

GDR [Jan89]. generation [Zeu84a]. Germany [AJ94, BJS93, DJS90, DJS91, EFW93, MT99, RSWZ98a]. GOSLER [JL95]. Grammatical [HS98]. Guest [BSVZ14, GTZ10, HSVZ13, KSZ14, Zeu97, ACZ16, JMSZ16, Zeu18]. Guided [ZL94, ZL95].

Having [Zeu92b, Zeu84a]. help [GJSZ18]. Highly [Zeu92b]. Hungary [CDG89]. hypotheses [LZ93a]. Hypothesis [LZ93b].

ICGI’98 [HS98]. Identification [KZ89b, KZ91, Zeu82, Zeu83c]. Ignoring [WZ94a]. Impact [LZ93d]. Improved [Zeu89b]. Incremental [CJLZ97, CJLZ99, LZ95a, LZ95b, LZ96b]. Independence [LZ93d]. Independent [LZ94b, LZ96a]. indexed [LZZ08]. Indistinguishability [JZ08]. Inductive [AJ94, BJS93, DJS90, Jan92, KZ85, KZ91, LGZ05, TZ95a, Zeu81, Zeu83b, Zeu83d, Zeu90a, Zeu91, Zeu06b, CSZZ08, DJS91, GJSZ18, Jan87, Jan89, TZ95b]. Inference [AJ94, HS98, Jan92, KZ89b, KZ91, KZ89a, TZ95a, Zeu81, Zeu83b, Zeu83d, Zeu90a, CSZZ08, GJSZ18, Jan87, Jan89, KZ85, KZ89c, LGZ05, TZ95b, Zeu91, Zeu06b]. influence [KKFZ14]. influenza [IZZ10]. Informant [LZ92d, LZ94a]. Informatics [BBP96]. Information [WZ95b, WZ92]. Intelligence [Yao95]. Internat [AJ94, Jan92]. International [AS96, BBP96, BJS93, CDG89, DJS90, HS98, JMSZ16, Jan89, JSZ95a, LM97, MT99, RSWZ98a, DJS91]. Introduction [JSZ95b, RSWZ98b, Zeu89a, KSZ14]. Iowa [HS98]. Issue [Zeu97, HSVZ13, JMSZ16, Zeu18].

Japan [JSZ95a, LM97]. Joint [Yao95]. June [BBP96].

k-Variable [RZ98f]. Karlsruhe [DJS90, DJS91]. Knowledge [JL95]. Knowledge-Based [JL95].

Lange [Zeu95, Zeu98]. Language [LZ92b, LZ92c, LZ92d, LZ93e, LZ93a, LZ94a, RZ00, ZLK92, Zeu94a, Zeu94b, Zeu95, Zeu98, LZK92, LZ93g, LZK96, ZLK95, Zeu06b]. Languages [ERS^{+}96, ERS^{+}97a, FZ95, FZ96, LZ93d, LZ93c, LZ93b, LZ94b, RZ97, RZ98b, RZ98e, RZ98f, SWZ97, TZ95a, WSZ93, WSZ94, ZL94, ZL95, ERS^{+}97c, ERS^{+}97b, ERS^{+}01, JKL^{+}00, JSZ12, LZ92a, LZ93f, LZ96a, LZZ08, RZ98c, RZ98d, RZ98g, TZ95b, WSZ95].

Large [Zeu90c]. Learn [WZ94a]. Learnability [LZ93d, LZ93b, GJSZ18, SZ99]. Learner [RZ00]. learners [BZ05, BZ06a, BZ06b, BZ11]. Learning [AJ94, AS96, BM98, CJLZ97, CJLZ99, CJR^{+}03, CJR^{+}06, ERS^{+}96, ERS^{+}97c, ERS^{+}97b, ERS^{+}97a, ERS^{+}01, FZ95, FZ96, FC90, GKP^{+}95, GLZ00, GTZ10, Hau92, JKL^{+}00, JKWZ01, JMSZ16, JL95, JSZ95a, KGZ^{+}90, LZ92a, LZ92b, LZ92c, LZ92d, LZ93e, LZ93f, LZ93c, LZ94a, LZ94b, LZ95a, LZ95b, LZ96b, LWZ96a, LWZ96b, LWZ96c, LZZ08, LM97, Pit93, RZ97, RZ98a, RZ98c, RZ98d, RZ98b, RZ99a, RSWZ98a, RZ98e, RZ98g, RZ98f, STA94, SZ02, Vit95, WZ94b, WZ95a, WZ95b, ZLK92, Zeu94a, Zeu94b, ZL94, ZL95, Zeu95, Zeu97, Zeu98, ZZ08, AZ08, BDCZ07, BSVZ14, CBRZ06, FZ11, FZ14, HSVZ13, JKWZ03, JSZ12, KSZ14, KKFZ14, LZK92, LZ93a, LZ93g, LZK96, LZ96a, RZ99b, RZ99c, SZ97, WZ92, ZLK95, Zeu03, Zeu06b, Zeu06a, Zeu18]. Lernen [Zeu93a, Zeu93b]. limit [Zeu03, Zeu06a]. Linear [RZ97, RZ98b, RZ98c, RZ98d]. Logic [BJS93, DJS90, DJS91, JZ08].

Mārtins [Zeu16]. Madison [BM98]. March [Vit95]. Mathematical [RZ16, Rov90]. Mathematics [RZ16]. may [WZ94a]. Memorial [BBP96]. memory [SZ97]. memoryless [BZ06a]. Mind [LZ93e, LZ93c, LZ95c, BZ05, LZ92a, LZ93f, LZ95e]. Mining [CJLZ97, CJLZ99]. Modeling [LZ95a]. modulo [Zeu92b]. Monomials [RZ99a, RZ99c]. Monotonic [LZ92b, LZ92c, LZ92d, LZ93g, LZK96, ZLK92, LZK92, ZLK95]. Monotonicity [LZ94a, LZ95c, LZ95e, LZ95d]. Monte [KZ89b]. Monte-Carlo [KZ89b]. Much [WZ95b, WZ92].

Nearly [Zeu92a]. non [SZ99, SZ02]. non-recursive [SZ99, SZ02].

Nonboundability [Zeu84b].

nonconstructivity [FZ11, JSZ12].

Nonmonotonic

[BJS93, DJS90, DJS91, LZ93g]. normalized [IZZ10]. note [JZ10a]. Novosibirsk [BBP96]. Number [LZ93e, LZ93c, Zeu92b].

Obituary [Zeu16]. October [AJ94, Jan89]. One [ERS^{+}96, ERS^{+}97a, KZ91, RZ97, RZ98b, RZ00, ERS^{+}97c, ERS^{+}97b, ERS^{+}01, JZ09, RZ98c, RZ98d]. One-Sided [KZ91]. One-Variable [ERS^{+}96, ERS^{+}97a, RZ97, RZ98b, RZ00, ERS^{+}97c, ERS^{+}97b, ERS^{+}01, RZ98c, RZ98d]. only [WZ94a, Zeu92b]. Open [Zeu90a, Zeu91]. operators [Zeu84b, Zeu84a]. Optimal [RZ00, Zeu90a, Zeu92a, Zeu91]. Optimizers [Zeu88]. Order [LZ93d, JZ08, JZ10b, JZ12]. Otzenhausen [RSWZ98a].

Parallel [ERS^{+}97a, Zeu90c, Zeu90b, Zeu92b, ERS^{+}97c, ERS^{+}01, Zeu89b]. Pattern [ERS^{+}96, ERS^{+}97a, RZ97, RZ98b, RZ00, RZ98e, RZ98f, Zeu94a, Zeu94b, Zeu95, Zeu98, ERS^{+}97c, ERS^{+}97b, ERS^{+}01, RZ98c, RZ98d, RZ98g]. patterns [CJR^{+}03, CJR^{+}06]. Pennsylvania [Hau92]. Perspectives [BBP96]. Pittsburgh [Hau92]. Polynomial [Zeu90c, CJR^{+}03, CJR^{+}06]. Positive [ERS^{+}96, FZ95, FZ96, LZ95a, LZ96b, RZ98e, RZ98f, TZ95a, JSZ12, LZZ08, RZ98g, TZ95b]. posteriori [Zeu83b]. Power [LZ92b, Zeu88]. Powers [Zeu90c]. Predicates [SWZ97, WSZ93, WSZ94, WSZ95]. Preface [JMSZ16, Zeu18]. Preserving [ZLK92, LZK92]. Prime [Zeu92b]. Probabilistic [KZ91]. Problems [Zeu90a, Zeu91]. Proc [BM98, FC90, Hau92, Pit93]. proceedings [CDG89, DJS91, EFW93, Jan87, Jan89, Rov90]. processes [KKFZ14]. program [Zeu84a]. Programmen [Zeu83a]. Programs [Zeu81, Zeu82, Zeu83d, Zeu90a, KZ85, Zeu83c, Zeu91]. properties [JZ09, JZ10b].

quantifier [JZ09]. quantifiers [JZ10b]. Queries [ERS^{+}97a, GKP^{+}95, ERS^{+}97c, ERS^{+}01, KGZ^{+}90]. Query [KZ89a, KZ89c].

Rūsins [Zeu16]. Ramsey [JZ10a]. randomized [BZ06a, BZ06b, BZ11]. Rearrangement [LZ94b, LZ96a]. Rearrangement-Independent [LZ94b, LZ96a]. Recursive [FZ95, FZ96, LZ93d, LZ93c, LZ93b, LZ94b, WSZ93, Zeu83b, Zeu84a, Zeu88, ZL94, ZL95, FZ11, FZ14, GLZ00, JKWZ01, LZ92a, LZ93f, LZ96a, LGZ05, LZZ08, SZ99, SZ02, WSZ95, ZZ08]. Refined [KZ89c, KZ89a, LZ95b]. refutably [JKWZ01, JKWZ03]. regular [CJR^{+}03, CJR^{+}06]. Reingold [Zeu92a]. Reinhardsbrunn [AJ94, BJS93, Jan89]. Relational [JZ09]. Relations [KZ89b]. Reliable [KZ89b, KZ91]. reliably [KZ85]. Report [Zeu94c, JL95]. respect [Zeu84a, Zeu95, Zeu98]. restricted [BZ05]. Review [Zeu89a, Zeu92a]. Rietz [Jan87]. ring [Zeu89b]. Rolf [CSZZ08]. Russia [BBP96].

Santa [Pit93]. schnellen [Zeu83a]. Science [EFW93, MT99, Rov90]. Searching [Zeu92a]. Second [BBP96]. Sendai [LM97]. Set [LZ94b, LZ96a]. Set-Driven [LZ94b, LZ96a]. Shen [Zeu92a]. shot [GJSZ18]. Sided [KZ91]. size [FZP13]. Small [Zeu92b]. Space [LZ93b, LZ93a]. Spain [Vit95]. Special [Zeu97, JMSZ16, Zeu18, HSVZ13]. Sprachen [Zeu93a, Zeu93b]. STACS [EFW93]. STACS’99 [MT99]. standardisers [GJSZ18]. stochastic [Zeu06a]. Stochastically [RZ98e, RZ98f, RZ98g]. strategies [KZ85]. subclass [CJR^{+}03, CJR^{+}06]. Survey [Zeu90a, LZZ08, Zeu91, ZZ08]. Sydney [AS96]. Symposium [EFW93, MT99]. Synthese [Zeu83a]. Synthesis [Zeu83d]. System [BBP96]. Systems [JL95]. Szeged [CDG89].

Teaching

[BZ05, BZ06a, BZ06b, BZ11, BZ09]. Teams [GKP^{+}95, KGZ^{+}90]. technology [KKFZ14]. testability [JZ10a]. Testable [JZ12, JZ09]. their [LZ92c]. Theoretical [EFW93, MT99]. Theory [AJ94, AS96, BM98, FC90, GTZ10, Hau92, JMSZ16, JSZ95a, LM97, Pit93, RSWZ98a, STA94, Vit95, Zeu97, BDCZ07, BSVZ14, CBRZ06, CDG89, HSVZ13, KSZ14, Zeu18]. Tight [RZ99a, RZ99c]. Time [RZ97, RZ98b, Zeu95, Zeu98, CJR^{+}03, CJR^{+}06, RZ98c, RZ98d]. Too [WZ92, WZ95b]. Total [Zeu95, Zeu98, Zeu84b]. Tour [ZL94, ZL95]. Trading [LZ95e, LZ95c, LZ95d]. Trier [MT99]. Two [TZ95a, TZ95b]. Types [LZ92c].

ultrametric [FZ14]. Unbounded [Zeu92a]. uniform [SZ99]. Unifying [LZ92d]. universal [JZ09]. Untestable [JZ10b, JZ12]. USA [BM98, HS98, Pit93].

Variable [ERS^{+}96, ERS^{+}97a, RZ97, RZ98b, RZ00, RZ98e, RZ98f, ERS^{+}97c, ERS^{+}97b, ERS^{+}01, RZ98c, RZ98d, RZ98g]. Variations [TZ95a, TZ95b]. various [LZ94a]. verifiers [GJSZ18]. versus [LZ93g, LZ95e, LZ95c, LZ95d, Zeu84a]. Very [ERS^{+}97a, Zeu90c, ERS^{+}97c, ERS^{+}97b, ERS^{+}01]. Via [GKP^{+}95, KGZ^{+}90]. virus [IZZ10].

Way [WZ94a]. Wendisch [Jan87]. Wendisch-Rietz [Jan87]. Wiehagen [CSZZ08, Zeu95, Zeu98]. Wisconsin [BM98]. without [BZ06a]. working [KZ85]. Workshop [AJ94, AS96, BJS93, DJS90, FC90, Hau92, Jan89, Jan92, JSZ95a, LM97, DJS91]. Würzburg [EFW93].

years [CSZZ08].

Zur [Zeu83a].

References

Auer:2016:GEF

[ACZ16] Peter Auer, Alexander Clark, and Thomas Zeugmann. Guest Editors’ foreword. Theoretical Computer Science, 650(??): 1–3, October 18, 2016. CO-DEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (elec-

tronic).  URL http://www.
sciencedirect.com/science/
article/pii/S0304397516303723.

Arikawa:1994:ALT

[AJ94] S. Arikawa and K. P. Jantke, editors. Algorithmic Learning Theory, 4th Internat. Workshop on Analogical and Inductive Inference, AII’94, 5th Internat. Workshop on Algorithmic Learning Theory, ALT’94, Reinhardsbrunn Castle, Germany, October 1994, volume 872 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, October 1994.

Arikawa:1996:ALT

[AS96] S. Arikawa and A. Sharma, editors. Algorithmic Learning Theory, 7th International Workshop, ALT’96, Sydney, Australia, volume 1160 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, October 1996.

Akama:2008:CCL

[AZ08] Yohji Akama and Thomas Zeugmann. Consistent and coherent learning with δ-delay. Inform. Comput., 206(11):1362– 1374, 2008. CODEN INFCEC. ISSN 0890-5401 (print), 1090-2651 (electronic).

Bjorner:1996:PSI

[BBP96] D. Bjørner, M. Broy, and I. V. Pottosin, editors. Perspectives of System Informatics, Second International Andrei Ershov Memorial Conference, Akademgorodok, Novosibirsk, Russia, June 1996, volume 1181 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, October 1996.

Ben-David:2007:FAL

[BDCZ07] Shai Ben-David, John Case, and Thomas Zeugmann. Foreword [Algorithmic learning theory]. Theoretical Computer Science, 382(3):167–169, 2007. CO-DEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Brewka:1993:NIL

[BJS93] G. Brewka, K. P. Jantke, and P. H. Schmitt, editors. Nonmonotonic and Inductive Logic, 2nd International Workshop, Reinhardsbrunn, Dezember 1991, Germany, volume 659 of Lecture Notes in Artificial Intelligence.

Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1993.

Bartlett:1998:CPA

[BM98] P. Bartlett and Y. Mansour, editors. Proc. of the Eleventh Annual Conference on Computational Learning Theory, Madison, Wisconsin, USA. ACM Press, New York, NY 10036, USA, 1998.

Bshouty:2014:GEF

[BSVZ14] Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, and Thomas Zeugmann. Guest editors’ foreword [Algorithmic learning theory]. Theoretical Computer Science, 558:1–4, 2014. CODEN TC-SCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Balbach:2005:TLR

[BZ05] Frank J. Balbach and Thomas Zeugmann. Teaching learners with restricted mind changes. In Algorithmic learning theory, volume 3734 of Lecture Notes in Comput. Sci., pages 474– 489. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2005.

Balbach:2006:TMR

[BZ06a] Frank J. Balbach and Thomas Zeugmann. Teaching memoryless randomized learners without feedback. In Algorithmic learning theory, volume 4264 of Lecture Notes in Comput. Sci., pages 93– 108. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2006.

Balbach:2006:TRL

[BZ06b] Frank J. Balbach and Thomas Zeugmann. Teaching randomized learners. In Learning theory, volume 4005 of Lecture Notes in Comput. Sci., pages 229– 243. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2006.

Balbach:2009:RDA

[BZ09] Frank J. Balbach and Thomas Zeugmann. Recent developments in algorithmic teaching. In Language and automata theory and applications, volume 5457 of Lecture Notes in Comput. Sci., pages 1–18. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2009.

Balbach:2011:TRL

[BZ11] Frank J. Balbach and Thomas Zeugmann. Teaching randomized learners with feedback. Inform. Comput., 209(3):296–319, 2011. CODEN INFCEC. ISSN 0890-5401 (print), 1090-2651 (electronic).

Cesa-Bianchi:2006:FAL

[CBRZ06] Nicolò Cesa-Bianchi, Rüdiger Reischuk, and Thomas Zeugmann. Foreword [Algorithmic learning theory (ALT 2002)]. Theoretical Computer Science, 350(1):1–2, 2006. CODEN TC-SCDI. ISSN 0304-3975 (print), 1879-2294 (electronic). Held in Lübeck, November 24–26, 2002.

Csirik:1989:FCT

[CDG89] J. Csirik, Janos Demetrovics, and F. Gecseg, editors. Fundamentals of computation theory: International Conference, FCT’89, Szeged, Hungary, August 21– 25, 1989: proceedings, volume 380 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1989. ISBN 0-387-51498-8 (West Germany). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA75.5 I5731 1989. DM69.00.

Case:1997:ICL

[CJLZ97] J. Case, S. Jain, S. Lange, and T. Zeugmann. Incremental concept learning for bounded data mining. Technical Report DOI-TR-136, Department of Informatics, Kyushu University, Kyushu, Japan, 1997.

Case:1999:ICL

[CJLZ99] John Case, Sanjay Jain, Steffen Lange, and Thomas Zeugmann. Incremental concept learning for bounded data mining. Inform. Comput., 152(1):74–110, 1999. CODEN INFCEC. ISSN 0890-5401 (print), 1090-2651 (electronic). To appear.

Case:2003:LSR

[CJR^{+}03] John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann. Learning a subclass of regular patterns in polynomial time. In Algorithmic learning theory, volume 2842 of Lecture Notes in Comput. Sci., pages 234–246. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2003.

Case:2006:LSR

[CJR^{+}06] John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann. Learning a subclass of regular patterns in polynomial time. Theoretical Computer Science, 364(1): 115–131, 2006. CODEN TC-SCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Case:2008:FFY

[CSZZ08] John Case, Takeshi Shinohara, Thomas Zeugmann, and Sandra Zilles. Foreword [Forty years of inductive inference. Dedicated to the 60th birthday of Rolf Wiehagen]. Theoretical Computer Science, 397(1-3):1–3, 2008. CO-DEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Dix:1990:NIL

[DJS90] J. Dix, K. P. Jantke, and P. H. Schmitt, editors. Nonmonotonic and Inductive Logic, 1st International Workshop, Karlsruhe, Germany, volume 543 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1990.

Dix:1991:NIL

[DJS91] J. Dix, K. P. Jantke, and P. H. Schmitt, editors. Nonmonotonic and inductive logic: 1st international workshop, Karlsruhe, Germany, December 4–7, 1990, proceedings, volume 543 of Lecture Notes in Computer Science and Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1991. ISBN 3-540-54564-6, 0-387-54564-6. ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA76.63 .N66 1991. Proceedings of the First International Workshop on Nonmonotonic and Inductive Inference.

Enjalbert:1993:SAS

[EFW93] P. Enjalbert, A. Finkel, and K. W. Wagner, editors. STACS 93: 10th Annual Symposium on Theoretical Aspects of Computer Science, Würzburg, Germany, February 25–27, 1993: proceedings, volume 665 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1993. ISBN 0-387-56503-5 (U.S.). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA267.A1 L43 no.665.

Erlebach:1996:ELO

[ERS^{+}96] T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger, and T. Zeugmann. Efficient learning of one-variable pattern languages from positive examples. Technical Report DOI-TR-128, Department of Informatics, Kyushu University, Kyushu, Japan, 1996.

Erlebach:1997:OP

[ERS^{+}97a] T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger, and T. Zeugmann. Learning onevariable pattern languages very efficiently on average, in parallel, and by asking queries. In Li and Maruoka [LM97], pages 260–276.

Erlebach:1997:LOVb

[ERS^{+}97b] Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and Thomas Zeugmann. Learning one-variable pattern languages very efficiently. Sūrikaisekikenkyūsho Kōkyūroku, (992):47–57, 1997. The theory of computing and its applications (Japanese) (Kyoto, 1997).

Erlebach:1997:LOV

[ERS^{+}97c] Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and Thomas Zeugmann. Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries. In Algorithmic learning theory (Sendai, 1997), volume 1316 of Lecture Notes in Comput. Sci., pages 260– 276. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1997.

Erlebach:2001:LOV

[ERS^{+}01] Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and Thomas Zeugmann. Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries. Theoretical Computer Science, 261(1):119–156, 2001. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic). Algorithmic learning theory (Sendai, 1997).

Fulk:1990:CPA

[FC90] M. Fulk and J. Case, editors.

COLT’90, Proc. of the 3rd Annual ACM Workshop on Computational Learning Theory. Morgan Kaufmann, San Mateo, 1990.

Freivalds:1995:CLR

[FZ95] R. Freivalds and T. Zeugmann.

Co-learning of recursive languages from positive data. Technical Report RIFIS-TR-CS-110, RIFIS, Kyushu University 33, Kyushu, Japan, 1995.

Freivalds:1996:CLR

[FZ96] R. Freivalds and T. Zeugmann.

Co–learning of recursive languages from positive data. In Bjørner et al. [BBP96], pages 122–133.

Freivalds:2011:ANL

[FZ11] Rūsiņš Freivalds and Thomas Zeugmann. On the amount of nonconstructivity in learning recursive functions. In Theory and applications of models of computation, volume 6648 of Lecture Notes in Comput. Sci., pages 332–343. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2011.

Freivalds:2014:ALR

[FZ14] Rūsiņš Freivalds and Thomas Zeugmann. Active learning of recursive functions by ultrametric algorithms. In SOFSEM 2014: theory and practice of computer science, volume 8327 of Lecture Notes in Comput. Sci., pages 246–257. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2014.

Freivalds:2013:SCD

[FZP13] Rūsiņš Freivalds, Thomas Zeugmann, and Grant R. Pogosyan. On the size complexity of deterministic frequency automata. In Language and automata theory and applications, volume 7810 of Lecture Notes in Comput. Sci., pages 287–298. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2013.

Gao:2018:HBS

[GJSZ18] Ziyuan Gao, Sanjay Jain, Frank Stephan, and Thomas Zeugmann. On the help of bounded shot verifiers, comparators and standardisers for learnability in inductive inference. In Algorithmic Learning Theory 2018, volume 83 of Proc. Mach. Learn. Res. (PMLR), page 25. Proceedings of Machine Learning Research PMLR, ????, 2018.

Gasarch:1995:LQT

[GKP^{+}95] William I. Gasarch, Efim B. Kinber, Mark G. Pleszkoch, Carl H. Smith, and Thomas Zeugmann. Learning via queries with teams and anomalies. Fundamenta Informaticae, 23(1):67–89, January 1995. CODEN FUMAAJ. ISSN 0169-2968 (print), 1875-8681 (electronic).

Grieser:2000:LRC

[GLZ00] Gunter Grieser, Steffen Lange, and Thomas Zeugmann. Learning recursive concepts with anomalies. In Algorithmic learning theory (Sydney, 2000), volume 1968 of Lecture Notes in Comput. Sci., pages 101–115. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2000.

Gyorfi:2010:GEF

[GTZ10] László Györfi, György Turán, and Thomas Zeugmann. Guest editors’ foreword [Algorithmic Learning Theory (ALT 2008)]. Theoretical Computer Science, 411(29–30):2629–2631, 2010. CO-DEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic). Held in Budapest, October 13– 16, 2008.

Haussler:1992:PAA

[Hau92] D. Haussler, editor. Proc. of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania. ACM Press, New York, NY 10036, USA, 1992.

Honavar:1998:ICGI

[HS98] V. Honavar and G. Slutzki, editors. Grammatical Inference, 4th International Colloquium, ICGI’98, Ames, Iowa, USA, volume 1433 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, July 1998.

Hutter:2013:GEF

[HSVZ13] Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann. Guest Editors’ foreword [Special issue on algorithmic learning theory]. Theoretical Computer Science, 473:1–3, 2013. CODEN TCSDIQ. ISSN 0304-3975 (print), 1879-2294 (electronic).

Ito:2010:CNC

[IZZ10] Kimihito Ito, Thomas Zeugmann, and Yu Zhu. Clustering the normalized compression distance for influenza virus data. In Algorithms and applications, volume 6060 of Lecture Notes in Comput. Sci., pages 130–146. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2010.

Jantke:1987:AII

[Jan87] K. P. Jantke, editor. Analogical and inductive inference: proceedings [Wendisch-Rietz, 1986], volume 265 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1987. ISBN 0-387-18081-8 (paperback). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN Q334 .I5771 1986.

Jantke:1989:AII

[Jan89] K. P. Jantke, editor. Analogical and inductive inference: International Workshop AII ’89, Reinhardsbrunn Castle, GDR, October, 1989: proceedings, volume 397 of Lecture Notes in Computer Science and Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1989. ISBN 3-540-51734-0 (Berlin), 0-387-51734-0 (New York). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN Q334 .I577 1989.

Jantke:1992:AII

[Jan92] K. P. Jantke, editor. Analogical and Inductive Inference, Internat. Workshop AII’92, volume 642 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1992.

Jain:2000:LLF

[JKL^{+}00] Sanjay Jain, Efim Kinber, Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann. Learning languages and functions by erasing. Theoretical Computer Science, 241(1-2):143–189, 2000. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic). Algorithmic learning theory (Sydney, 1996).

Jain:2001:LRF

[JKWZ01] Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann. Learning recursive functions refutably. In Algorithmic learning theory (Washington, DC, 2001), volume 2225 of Lecture Notes in Comput. Sci., pages 283–298. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc.,

Jain:2003:LFR

[JKWZ03] Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann. On learning of functions refutably. Theoretical Computer Science, 298(1):111–143, 2003. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic). Selected papers in honour of Setsuo Arikawa.

Jantke:1995:ALK

[JL95] K. P. Jantke and S. Lange, editors. Algorithmic Learning for Knowledge-Based Systems, GOSLER final report, volume 961 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1995.

Jain:2016:GEF

[JMSZ16] Sanjay Jain, Rémi Munos, Frank Stephan, and Thomas Zeugmann. Guest Editors’ foreword [preface to the special issue dedicated to the 24th International Conference on Algorithmic Learning Theory (ALT 2013)]. Theoretical Computer Science, 620(??):1–3, March 21, 2016. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (elec-

                                        tronic).  URL http://www.
                                        sciencedirect.com/science/
                                        article/pii/S0304397515009391.

Held at the National University of Singapore, Singapore, October 6–9, 2013.

Jantke:1995:ALT

[JSZ95a] K. P. Jantke, T. Shinohara, and T. Zeugmann, editors. Algorithmic Learning Theory, 6th International Workshop, ALT’95, Fukuoka, Japan, volume 997 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, October 1995.

Jantke:1995:EI

[JSZ95b] K. P. Jantke, T. Shinohara, and T. Zeugmann. Editors’ introduction. In Proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT’95, Fukuoka, Japan [JSZ95a], pages ix–xv.

Jain:2012:ANL

[JSZ12] Sanjay Jain, Frank Stephan, and Thomas Zeugmann. On the amount of nonconstructivity in learning formal languages from positive data. In Theory and applications of models of computation, volume 7287 of Lecture Notes in Comput. Sci., pages 423–434. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2012.

Jordan:2008:IFO

[JZ08] Skip Jordan and Thomas Zeugmann. Indistinguishability and first-order logic. In Theory and applications of models of computation, volume 4978 of Lecture Notes in Comput. Sci., pages 94– 104. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2008.

Jordan:2009:RPE

[JZ09] Charles Jordan and Thomas Zeugmann. Relational properties expressible with one universal quantifier are testable. In Stochastic algorithms: foundations and applications, volume 5792 of Lecture Notes in Comput. Sci., pages 141–155. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2009.

Jordan:2010:NTR

[JZ10a] Charles Jordan and Thomas Zeugmann. A note on the testability of Ramsey’s class. In Theory and applications of models of computation, volume 6108 of Lecture Notes in Comput. Sci., pages 296–307. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2010.

Jordan:2010:UPE

[JZ10b] Charles Jordan and Thomas Zeugmann. Untestable properties expressible with four first-order quantifiers. In Language and automata theory and applications, volume 6031 of Lecture Notes in Comput. Sci., pages 333– 343. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2010.

Jordan:2012:TUC

[JZ12] Charles Jordan and Thomas Zeugmann. Testable and untestable classes of first-order formulae. Journal of Computer and System Sciences, 78(5):1557– 1578, 2012. CODEN JCSSBM. ISSN 0022-0000 (print), 1090-2724 (electronic).

Kinber:1990:LQT

[KGZ^{+}90] E. B. Kinber, W. I. Gasarch, T. Zeugmann, M. K. Pleszkoch, and C. H. Smith. Learning via queries with teams and anomalies. In Fulk and Case [FC90], pages 327–337.

Kucevalovs:2014:ITL

[KKFZ14] Ilja Kucevalovs, Ojārs Krasts, Rūsiņš Freivalds, and Thomas Zeugmann. On the influence of technology on learning processes. Parallel Processing Letters, 24(2): 1440003:1–1440003:17, 2014. CO-DEN PPLTEE. ISSN 0129-6264.

Kivinen:2014:GEI

[KSZ14] Jyrki Kivinen, Csaba Szepesvári, and Thomas Zeugmann. Guest editors’ introduction [Algorithmic learning theory]. Theoretical Computer Science, 519:1–3, 2014. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Kinber:1985:IIA

[KZ85] Efim B. Kinber and Thomas Zeugmann. Inductive inference of almost everywhere correct programs by reliably working strategies. Elektronische Informationsverarbeitung und Kybernetik, 21(3):91–100, 1985. CO-DEN EIVKAX. ISSN 0013-5712.

Kinber:1993:RQI

[KZ89a] E. B. Kinber and T. Zeugmann. Refined query inference. In Jantke [Jan89], pages 148–160. ISBN 3-540-51734-0 (Berlin), 0-387-51734-0 (New York). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN Q334 .I577 1989.

Kinber:1989:MCI

[KZ89b] Efim Kinber and Thomas Zeugmann. Monte-Carlo inference and its relations to reliable frequency identification. In Csirik et al. [CDG89], pages 257–266. ISBN 0-387-51498-8 (West Germany). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA75.5 I5731 1989. DM69.00.

Kinber:1989:RQI

[KZ89c] Efim B. Kinber and Thomas Zeugmann. Refined query inference (extended abstract). In Analogical and inductive inference (Reinhardsbrunn Castle, 1989), volume 397 of Lecture Notes in Artificial Intelligence, pages 148– 160. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1989.

Kinber:1991:OSE

[KZ91] Efim B. Kinber and Thomas Zeugmann. One-sided error probabilistic inductive inference and reliable frequency identification. Inform. Comput., 92(2): 253–284, 1991. CODEN IN-FCEC. ISSN 0890-5401 (print), 1090-2651 (electronic).

Lange:2005:IIA

[LGZ05] Steffen Lange, Gunter Grieser, and Thomas Zeugmann. Inductive inference of approximations for recursive concepts. Theoretical Computer Science, 348(1): 15–40, 2005. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Li:1997:ALT

[LM97] M. Li and A. Maruoka, editors. Algorithmic Learning Theory, 8th International Workshop, ALT’97, Sendai, Japan, volume 1316 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, October 1997.

Lange:1996:LBE

[LWZ96a] S. Lange, R. Wiehagen, and T. Zeugmann. Learning by erasing. In Arikawa and Sharma [AS96], pages 228–241.

Lange:1996:LEa

[LWZ96b] S. Lange, R. Wiehagen, and T. Zeugmann. Learning by erasing. Technical Report RIFIS-TR-CS-122, RIFIS, Kyushu University 33, Kyushu, Japan, 1996.

Lange:1996:LEb

[LWZ96c] Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann. Learning by erasing. In Algorithmic learning theory (Sydney, 1996), volume 1160 of Lecture Notes in Comput. Sci., pages 228– 241. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1996.

Lange:1992:LRL

[LZ92a] S. Lange and T. Zeugmann. Learning recursive languages with bounded mind changes. Technical Report 16/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1992.

Lange:1992:PML

[LZ92b] S. Lange and T. Zeugmann. On the power of monotonic language learning. Technical Report 5/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1992.

Lange:1992:TML

[LZ92c] S. Lange and T. Zeugmann. Types of monotonic language learning and their characterization. In Haussler [Hau92], pages 377–390.

Lange:1992:UAM

[LZ92d] S. Lange and T. Zeugmann. A unifying approach to monotonic language learning on informant. In Jantke [Jan92], pages 244–259.

Lange:1993:LLD

[LZ93a] S. Lange and T. Zeugmann. Language learning in dependence on the space of hypotheses. In Pitt [Pit93], pages 127–136.

Lange:1993:LRLb

[LZ93b] S. Lange and T. Zeugmann. The learnability of recursive languages in dependence on the hypothesis space. Technical Report 20/93, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1993.

Lange:1993:LRLa

[LZ93c] S. Lange and T. Zeugmann.

Learning recursive languages with a bounded number of mind changes. Internat. Journal of Foundations of Computer Science, 4:157–178, 1993. CODEN IFCSEN. ISSN 0129-0541 (print), 1793-6373 (electronic).

Lange:1993:IOI

[LZ93d] S. Lange and T. Zeugmann. On the impact of order independence to the learnability of recursive languages. Technical Report ISIS-RR-93-17E, Research Report, FUJITSU Laboratories Ltd., Numazu, Numazu, Japan, 1993.

Lange:1993:LLB

[LZ93e] Steffen Lange and Thomas Zeugmann. Language learning with a bounded number of mind changes. In Enjalbert et al. [EFW93], pages 682–691. ISBN 0-387-56503-5 (U.S.). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA267.A1 L43 no.665.

Lange:1993:LRL

[LZ93f] Steffen Lange and Thomas Zeugmann. Learning recursive languages with bounded mind changes. Internat. Journal of Foundations of Computer Science, 4(2):157–178, 1993. CO-DEN IFCSEN. ISSN 0129-0541 (print), 1793-6373 (electronic).

Lange:1993:MVN

[LZ93g] Steffen Lange and Thomas Zeugmann. Monotonic versus nonmonotonic language learning. In Brewka et al. [BJS93], pages 254– 269.

Lange:1994:CLL

[LZ94a] S. Lange and T. Zeugmann. Characterization of language learning from informant under various monotonicity constraints. Journal of Experimental and Artificial Intelligence, 6: 73–94, 1994. CODEN JEAIEL. ISSN 0952-813x (print), 1362-3079 (electronic).

Lange:1994:SDR

[LZ94b] S. Lange and T. Zeugmann. Set-driven and rearrangementindependent learning of recursive languages. In Arikawa and Jantke [AJ94], pages 453–468.

Lange:1995:MIL

[LZ95a] S. Lange and T. Zeugmann. Modeling incremental learning from positive data. Technical Report RIFIS-TR-CS-117, RIFIS, Kyushu University 33, Kyushu, Japan, 1995.

Lange:1995:RIL

[LZ95b] S. Lange and T. Zeugmann. Refined incremental learning. In Yao [Yao95], pages 147–154.

Lange:1995:TMDa

[LZ95c] S. Lange and T. Zeugmann. Trading monotonicity demands versus mind changes. In Vitányi [Vit95], pages 125–139.

Lange:1995:TMDb

[LZ95d] Steffen Lange and Thomas Zeugmann. Trading monotonicity demands versus efficiency. Bulletin of Informatics and Cybernetics, 27(1):53–83, 1995. ISSN 0286-522X.

Lange:1995:TMD

[LZ95e] Steffen Lange and Thomas Zeugmann. Trading monotonicity demands versus mind changes. In Computational learning theory (Barcelona, 1995), volume 904 of Lecture Notes in Comput. Sci., pages 125–139. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1995.

Lange:1996:SDR

[LZ96a] S. Lange and T. Zeugmann.

Set-driven and rearrangementindependent learning of recursive languages. Math. Systems Theory, 29(6):599–634, 1996. CO-DEN MASTBA. ISSN 0025-5661.

Lange:1996:ILP

[LZ96b] Steffen Lange and Thomas Zeugmann. Incremental learning from positive data. Journal of Computer and System Sciences, 53 (1):88–103, 1996. CODEN JC-SSBM. ISSN 0022-0000 (print), 1090-2724 (electronic).

Lange:1992:CPM

[LZK92] S. Lange, T. Zeugmann, and S. Kapur. Class preserving monotonic and dual monotonic language learning. Technical Report 14/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1992.

Lange:1996:MDM

[LZK96] Steffen Lange, Thomas Zeugmann, and Shyam Kapur. Monotonic and dual monotonic language learning. Theoretical Computer Science, 155(2):365– 410, 1996. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Lange:2008:LIF

[LZZ08] Steffen Lange, Thomas Zeugmann, and Sandra Zilles. Learning indexed families of recursive languages from positive data: a survey. Theoretical Computer Science, 397(1-3):194–232, 2008. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Meinel:1999:STACS

[MT99] C. Meinel and S. Tison, editors. STACS’99, 16th International Symposium on Theoretical Aspects of Computer Science, Trier, Germany, volume 1563 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, March 1999.

Pitt:1993:PAA

[Pit93] L. Pitt, editor. Proc. of the 6th Annual ACM Conference on Computational Learning Theory, Santa Cruz, California, USA. ACM Press, New York, NY 10036, USA, 1993.

Rovan:1990:MFC

[Rov90] B. Rovan, editor. Mathematical foundations of computer science, 1990: Banská Bystrica, Czechoslovakia, August 27–31, 1990: proceedings, volume 452 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1990. ISBN 3-540-52953-5, 0-387-52953-5. ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA76.9.M35 M386 1990. Papers selected for presentation at the 15th Symposium on Mathematical Foundations of Computer Science - MFCS ’90.

Richter:1998:ALT

[RSWZ98a] M. M. Richter, C. H. Smith, R. Wiehagen, and T. Zeugmann, editors. Algorithmic Learning Theory, 9th International Conference, ALT’98, Otzenhausen, Germany, volume 1501 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, October 1998.

Richter:1998:EI

[RSWZ98b] M. M. Richter, C. H. Smith, R. Wiehagen, and T. Zeugmann. Editors’ introduction. In Proceedings of the 9th International Conference on Algorithmic Learning Theory, ALT’98, Otzenhausen, Germany [RSWZ98a], pages 1– 10.

Reischuk:1997:LOV

[RZ97] R. Reischuk and T. Zeugmann. Learning one-variable pattern languages in linear average time. Technical Report DOI-TR-140, Department of Informatics, Kyushu University, Kyushu, Japan, 1997.

Reischuk:1998:AAC

[RZ98a] R. Reischuk and T. Zeugmann. Analyzing the average-case behavior of conjunctive learning algorithms. Technical Report DOI-TR-153, Department of Informatics, Kyushu University, Kyushu, Japan, 1998.

Reischuk:1998:OP

[RZ98b] R. Reischuk and T. Zeugmann. Learning one-variable pattern languages in linear average time. In Bartlett and Mansour [BM98], pages 198–208.

Reischuk:1998:LOV

[RZ98c] Rüdiger Reischuk and Thomas Zeugmann. Learning one-variable pattern languages in linear average time. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (Madison, WI, 1998), pages 198–208 (electronic). ACM Press, New York, NY 10036, USA, 1998.

Reischuk:1998:LOVb

[RZ98d] Rüdiger Reischuk and Thomas Zeugmann. Learning one-variable pattern languages in linear average time. Sūrikaisekikenkyūsho Kōkyūroku, (1041):95–102, 1998. Algorithms and theory of computing (Japanese) (Kyoto, 1998).

Rossmanith:1998:LVPa

[RZ98e] P. Rossmanith and T. Zeugmann.

Learning k-variable pattern languages efficiently stochastically finite on average from positive data. Technical Report DOI-TR-145, Department of Informatics, Kyushu University, Kyushu, Japan, 1998.

Rossmanith:1998:PL

[RZ98f] P. Rossmanith and T. Zeugmann.

Learning k-variable pattern languages efficiently stochastically finite on average from positive data. In Honavar and Slutzki [HS98], pages 13–24.

Rossmanith:1998:LVPb

[RZ98g] Peter Rossmanith and Thomas Zeugmann. Learning k-variable pattern languages efficiently stochastically finite on average from positive data. In Grammatical inference (Ames, IA, 1998), volume 1433 of Lecture Notes in Comput. Sci., pages 13– 24. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1998.

Reischuk:1999:AV

[RZ99a] R. Reischuk and T. Zeugmann.

A complete and tight averagecase analysis of learning monomials. In Meinel and Tison [MT99], pages 414–423.

Reischuk:1999:AAC

[RZ99b] Rüdiger Reischuk and Thomas Zeugmann. Analyzing the average-case behavior of conjunctive learning algorithms.

Sūrikaisekikenkyūsho Kōkyūroku, (1093):123–129, 1999. Models of computation and algorithms (Japanese) (Kyoto, 1999).

Reischuk:1999:CTA

[RZ99c] Rüdiger Reischuk and Thomas Zeugmann. A complete and tight average-case analysis of learning monomials. In STACS 99 (Trier), volume 1563 of Lecture Notes in Comput. Sci., pages 414– 423. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1999.

Reischuk:2000:ACO

[RZ00] Rüdiger Reischuk and Thomas Zeugmann. An average-case optimal one-variable pattern language learner. Journal of Computer and System Sciences, 60 (2):302–335, April 2000. CO-DEN JCSSBM. ISSN 0022-0000 (print), 1090-2724 (elec-

tronic).  URL http://www.
sciencedirect.com/science/
article/pii/S0022000099916690.

Eleventh Annual Conference on Computational Learning Theory (Madison, WI, 1998).

Romisch:2016:MAM

[RZ16] Werner Römisch and Thomas Zeugmann. Mathematical Analysis and the Mathematics of Computation. Springer, Cham, Switzerland, 2016. ISBN 3-319-42753-9 (hardcover), 3-319-42755-5 (e-book). xxiii +

704 pp. URL http://link.
springer.com/10.1007/978-3-
319-42755-3.

Shawe-Taylor:1994:CLT

[STA94] J. Shawe-Taylor and M. Anthony, editors. Computational Learning Theory: Eurocolt ’93, volume New Series Number 53 of The Institute of Mathematics and its Applications Conference Series. Oxford University Press, Walton Street, Oxford OX2 6DP, UK, 1994. ISBN 0-19-853492-2.

Smith:1997:CPL

[SWZ97] C. H. Smith, R. Wiehagen, and T. Zeugmann. Classifying predicates and languages. Internat. Journal of Foundations of Computer Science, 8:15–41, 1997. CO-DEN IFCSEN. ISSN 0129-0541 (print), 1793-6373 (electronic).

Sakamoto:1997:LAD

[SZ97] Hiroshi Sakamoto and Thomas Zeugmann. The learning ability of deterministic automata with finite memory. Sūrikaisekikenkyūsho Kōkyūroku, (992):36–41, 1997. The theory of computing and its applications (Japanese) (Kyoto, 1997).

MR1788537

[SZ99] Frank Stephan and Thomas Zeugmann. On the uniform learnability of approximations to nonrecursive functions. In Algorithmic learning theory (Tokyo, 1999), volume 1720 of Lecture Notes in Comput. Sci., pages 276–290. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1999.

Stephan:2002:LCA

[SZ02] Frank Stephan and Thomas Zeugmann. Learning classes of approximations to non-recursive functions. Theoretical Computer Science, 288(2):309–341, 2002. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic). Algorithmic learning theory (Tokyo, 1999).

Tabe:1995:TVIa

[TZ95a] T. Tabe and T. Zeugmann. Two variations of inductive inference of languages from positive data. Technical Report RIFIS-TR-CS-105, RIFIS, Kyushu University 33, Kyushu, Japan, 1995.

Tabe:1995:TVIb

[TZ95b] Takashi Tabe and Thomas Zeugmann. Two variations of inductive inference of languages from positive data. Sūrikaisekikenkyūsho Kōkyūroku, (906):73–80, 1995. Algorithms and computational complexity theory (Japanese) (Kyoto, 1995).

Vitanyi:1995:CLT

[Vit95] P. Vitányi, editor. Computational Learning Theory, 2nd European Conference, EuroColt’95, Barcelona, Spain, March 1995, volume 904 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, March 1995.

Wiehagen:1993:CRP

[WSZ93] R. Wiehagen, C. H. Smith, and T. Zeugmann. Classifying recursive predicates and languages. Technical Report 21/93, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1993.

Wiehagen:1994:CPL

[WSZ94] R. Wiehagen, C. H. Smith, and T. Zeugmann. Classification of predicates and languages. In Shawe-Taylor and Anthony [STA94], pages 171–181. ISBN 0-19-853492-2.

Wiehagen:1995:CRP

[WSZ95] R. Wiehagen, C. H. Smith, and T. Zeugmann. Classifying recursive predicates and languages. In Jantke and Lange [JL95], pages 177–192.

Wiehagen:1992:TMI

[WZ92] Rolf Wiehagen and Thomas Zeugmann. Too much information can be too much for learning efficiently. In Analogical and inductive inference (Dagstuhl Castle, 1992), volume 642 of Lecture Notes in Artificial Intelligence, pages 72–86. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1992.

Wiehagen:1994:IDM

[WZ94a] R. Wiehagen and T. Zeugmann.

Ignoring data may be the only way to learn efficiently. Journal of Experimental and Artificial Intelligence, 6:131–144, 1994. CO-DEN JEAIEL. ISSN 0952-813x (print), 1362-3079 (electronic).

Wiehagen:1994:LC

[WZ94b] R. Wiehagen and T. Zeugmann. Learning and consistency. Technical Report 28/94, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1994.

Wiehagen:1995:LC

[WZ95a] R. Wiehagen and T. Zeugmann. Learning and consistency. In Jantke and Lange [JL95], pages 1–24.

Wiehagen:1995:TMI

[WZ95b] R. Wiehagen and T. Zeugmann. Too much information can be too much for efficient learning. In Jantke [Jan92], pages 72–86.

Yao:1995:AJC

[Yao95] Xin Yao, editor. 8th Australian Joint Conference on Artificial Intelligence. World Scientific Publishing Co., Singapore; Philadelphia, PA, USA; River Edge, NJ, USA, 1995.

Zeugmann:1981:IIF

[Zeu81] T. Zeugmann. Inductive inference of fast programs. In Proc. Workshop on Algorithms and Computing Theory. Technical University of Poznan, Poznan, Poland, 1981.

Zeugmann:1982:FIF

[Zeu82]  T. Zeugmann.  On the fi-

nite identification of fastest programs. In Proc. Symposium on Mathematical Foundations of Computer Science. Humboldt-Universität, Berlin, Germany, 1982. Seminarbericht Nr. 52.

Zeugmann:1983:ASS

[Zeu83a] T. Zeugmann. Zur algorithmischen Synthese von schnellen Programmen. PhD thesis, Humboldt-Universität zu Berlin, Sektion Mathematik, Berlin, Germany, 1983.

Zeugmann:1983:PCI

[Zeu83b] Thomas Zeugmann. A-posteriori characterizations in inductive inference of recursive functions. Elektronische Informationsverarbeitung und Kybernetik, 19(10– 11):559–594, 1983. CODEN EIVKAX. ISSN 0013-5712.

Zeugmann:1983:FIF

[Zeu83c] Thomas Zeugmann. On the finite identification of fastest programs. In Symposium on mathematical foundations of computer science (Diedrichshagen, 1982), volume 52 of Seminarberichte, pages 151–159. Humboldt Univ. Berlin, 1983.

Zeugmann:1983:SFP

[Zeu83d] Thomas Zeugmann. On the synthesis of fastest programs in inductive inference. Elektronische Informationsverarbeitung und Kybernetik, 19(12):625–642, 1983. CODEN EIVKAX. ISSN 0013-5712.

Zeugmann:1984:ROV

[Zeu84a] Th. Zeugmann. Recursive operators versus recursive functions with respect to the generation of classes of functions having a fastest program. In Algebraic and logical foundations of programming, volume 67 of Weiterbildungszentrum Math. Kybernet. Rechentech. Informationsverarbeitung, pages 75–85. Tech. Univ. Dresden, Dresden, 1984.

Zeugmann:1984:NTE

[Zeu84b] Thomas Zeugmann. On the nonboundability of total effective operators. Zeitschr. f. math. Logik und Grundlagen d. Math. Bd., 30(2):169–172, 1984. CODEN ZMLGAQ. ISSN 0044-3050.

Zeugmann:1987:BC

[Zeu87] Thomas Zeugmann. On Barzdin’s conjecture. In Jantke [Jan87], pages 220–227. ISBN 0-387-18081-8 (paperback). ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN Q334 .I5771 1986.

Zeugmann:1988:PRO

[Zeu88] Thomas Zeugmann. On the power of recursive optimizers. Theoretical Computer Science, 62 (3):289–310, 1988. CODEN TC-SCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Zeugmann:1989:RBC

[Zeu89a] T. Zeugmann. Review of Baase, Computer Algorithms: Introduction to Design and Analysis (2nd ed.) (1988). SIGACT News (ACM Special Interest Group on Automata and Computability Theory), 20, 1989. CO-DEN SIGNDM. ISSN 0163-5700 (print), 1943-5827 (electronic).

Zeugmann:1989:IPC

[Zeu89b] Thomas Zeugmann. Improved parallel computations in the ring Z/p^{α}. J. Inform. Process. Cybernet., 25(10):543–547, 1989. ISSN 0863-0593. Colloquium on Computation Theory (CCT ’88) (Berlin, 1988).

Zeugmann:1990:IIO

[Zeu90a] T. Zeugmann. Inductive inference of optimal programs: A survey and open problems. In Dix et al. [DJS90], pages 208–222.

Zeugmann:1990:PA

[Zeu90b] T. Zeugmann. Parallel algorithms. In Encyclopedia of Computer Science and Technology, volume 21 (Supplement 6), pages 223–244. Marcel Dekker, New York, NY, USA, 1990.

Zeugmann:1990:CLP

[Zeu90c] Thomas Zeugmann. Computing large polynomial powers very fast in parallel. In Rovan [Rov90], pages 538–544. ISBN 3-540-52953-5, 0-387-52953-5. ISSN 0302-9743 (print), 1611-3349 (electronic). LCCN QA76.9.M35 M386 1990. Papers selected for presentation at the 15th Symposium on Mathematical Foundations of Computer Science - MFCS ’90.

Zeugmann:1991:IIO

[Zeu91] Thomas Zeugmann. Inductive inference of optimal programs: a survey and open problems. In Nonmonotonic and inductive logic (Karlsruhe, 1990), volume 543 of Lecture Notes in Comput. Sci., pages 208–222. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 1991.

Zeugmann:1992:RRS

[Zeu92a] T. Zeugmann. Review of Reingold & Shen, more nearly optimal algorithms for unbounded searching (1991). SIGACT News (ACM Special Interest Group on Automata and Computability Theory), 23, 1992. CO-DEN SIGNDM. ISSN 0163-5700 (print), 1943-5827 (electronic).

Zeugmann:1992:HPC

[Zeu92b] Thomas Zeugmann. Highly parallel computations modulo a number having only small prime factors. Inform. Comput., 96 (1):95–114, 1992. CODEN IN-FCEC. ISSN 0890-5401 (print), 1090-2651 (electronic).

Zeugmann:1993:ALF

[Zeu93a] T. Zeugmann. Algorithmisches Lernen von Funktionen und Sprachen. Technical Report Habilitationsschrift, Technische Hochschule Darmstadt, Fachbereich Informatik, Darmstadt, Germany, 1993.

Zeugmann:1993:IAL

[Zeu93b] T. Zeugmann. Algorithmisches Lernen von Funktionen und Sprachen. Technical Report Habilitationsschrift, Technische Hochschule Darmstadt, Fachbereich Informatik, Darmstadt, Germany, 1993.

Zeugmann:1994:AAP

[Zeu94a] T. Zeugmann. Average-case analysis of pattern language learning algorithms. In Arikawa and Jantke [AJ94], pages 8–9.

Zeugmann:1994:ACA

[Zeu94b] T. Zeugmann. Average-case analysis of pattern language learning algorithms. In Proc. 4th Internat. Workshop on Analogical and Inductive Inference, volume 872 of Lecture Notes in Artificial Intelligence, pages 8–9. Springer-Verlag, Berlin, Heidelberg, New York, Tokyo, 1994.

Zeugmann:1994:RC

[Zeu94c] T. Zeugmann. Report on COLT 1994. SIGACT News (ACM Special Interest Group on Automata and Computability Theory), 25, 1994. CODEN SIGNDM. ISSN 0163-5700 (print), 1943-5827 (electronic).

Zeugmann:1995:LWP

[Zeu95] T. Zeugmann. Lange and Wiehagen’s pattern language learning algorithm: An average-case analysis with respect to its total learning time. Technical Report RIFIS-TR-CS-111, RIFIS, Kyushu University 33, Kyushu, Japan, 1995.

Zeugmann:1997:SIA

[Zeu97] T. Zeugmann. Special issue on algorithmic learning theory, (guest editor). Theoretical Computer Science, 185(1), 1997. CO-DEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Zeugmann:1998:LWP

[Zeu98] Thomas Zeugmann. Lange and Wiehagen’s pattern language learning algorithm: An averagecase analysis with respect to its total learning time. Annals of Mathematics and Artificial Intelligence, 23(1-2):117–145, 1998. CODEN AMAIEC. ISSN 1012-2443 (print), 1573-7470 (electronic). ALT 94 and AII 94 (Reinhardsbrunn Castle).

Zeugmann:2003:CLL

[Zeu03] Thomas Zeugmann. Can learning in the limit be done efficiently? In Algorithmic learning theory, volume 2842 of Lecture Notes in Comput. Sci., pages 17– 38. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2003.

Zeugmann:2006:LLS

[Zeu06a] Thomas Zeugmann. From learning in the limit to stochastic finite learning. Theoretical Computer Science, 364(1):77–97, 2006. CO-DEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).

Zeugmann:2006:IIL

[Zeu06b] Thomas Zeugmann. Inductive inference and language learning. In Theory and applications of models of computation, volume 3959 of Lecture Notes in Comput. Sci., pages 464–473. Springer-Verlag, Berlin, Germany / Heidelberg, Germany / London, UK / etc., 2006.

Zeugmann:2016:ORM

[Zeu16] Thomas Zeugmann. Obituary:

Rūsiņš Mārtiņš Freivalds (1942– 2016). Bull. Eur. Assoc. Theor. Comput. Sci. (EATCS), (118): 17–20, 2016. ISSN 0252-9742.

Zeugmann:2018:GEF

[Zeu18] Thomas Zeugmann. Guest Editor’s foreword [Preface: special issue on learning theory and complexity]. Theoretical Computer Science, 733(??):1–3, ???? 2018. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (elec-

tronic).  URL http://www.
sciencedirect.com/science/
article/pii/S0304397518302718.

Zeugmann:1994:GTA

[ZL94] T. Zeugmann and S. Lange. A guided tour across the boundaries of learning recursive languages. Technical Report 26/94, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, Leipzig, Germany, 1994.

Zeugmann:1995:GTA

[ZL95] T. Zeugmann and S. Lange. A guided tour across the boundaries of learning recursive languages. In Jantke and Lange [JL95], pages 193–262.

Zeugmann:1992:CCP

[ZLK92] T. Zeugmann, S. Lange, and S. Kapur. Characterizations of class preserving monotonic and dual monotonic language learning. Technical Report IRCS 92 - 24, Institute for Research in Cognitive Science, Univ. Pennsylvania, Philadelphia, PA, USA, 1992.

Zeugmann:1995:CMD

[ZLK95] Thomas Zeugmann, Steffen Lange, and Shyam Kapur. Characterizations of monotonic and dual monotonic language learning. Inform. Comput., 120(2): 155–173, 1995. CODEN IN-FCEC. ISSN 0890-5401 (print), 1090-2651 (electronic).

Zeugmann:2008:LRF

[ZZ08] Thomas Zeugmann and Sandra Zilles. Learning recursive functions: a survey. Theoretical Computer Science, 397(1-3):4– 56, 2008. CODEN TCSCDI. ISSN 0304-3975 (print), 1879-2294 (electronic).