Thomas Zeugmann

Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann: Guest Editors' foreword. Theor. Comput. Sci. 473: 1-3 (2013)

Rusins Freivalds, Thomas Zeugmann, Grant R. Pogosyan: On the Size Complexity of Deterministic Frequency Automata. LATA 2013: 287-298

Charles Jordan, Thomas Zeugmann: Testable and untestable classes of first-order formulae. J. Comput. Syst. Sci. 78(5): 1557-1578 (2012)

Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann: Editors' Introduction. ALT 2012: 1-11

Sanjay Jain, Frank Stephan, Thomas Zeugmann: On the Amount of Nonconstructivity in Learning Formal Languages from Positive Data. TAMC 2012: 423-434

Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann (Eds.): Algorithmic Learning Theory - 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings. Lecture Notes in Computer Science 7568, Springer 2012, isbn 978-3-642-34105-2

Frank J. Balbach, Thomas Zeugmann: Teaching randomized learners with feedback. Inf. Comput. 209(3): 296-319 (2011)

Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann: Editors' Introduction. ALT 2011: 1-13

Frank Stephan, Ryo Yoshinaka, Thomas Zeugmann: On the Parameterised Complexity of Learning Patterns. ISCIS 2011: 277-281

Rusins Freivalds, Thomas Zeugmann: On the Amount of Nonconstructivity in Learning Recursive Functions. TAMC 2011: 332-343

Charles Jordan, Thomas Zeugmann: Untestable Properties in the Kahr-Moore-Wang Class. WoLLIC 2011: 176-186

Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (Eds.): Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings. Lecture Notes in Computer Science 6925, Springer 2011, isbn 978-3-642-24411-7

László Györfi, György Turán, Thomas Zeugmann: Guest editors' foreword. Theor. Comput. Sci. 411(29-30): 2629-2631 (2010)

Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann: Editors' Introduction. ALT 2010: 1-10

Kimihito Ito, Thomas Zeugmann, Yu Zhu: Clustering the Normalized Compression Distance for Influenza Virus Data. Algorithms and Applications 2010: 130-146

Kimihito Ito, Thomas Zeugmann, Yu Zhu: Recent Experiences in Parameter-Free Data Mining. ISCIS 2010: 365-371

Charles Jordan, Thomas Zeugmann: Untestable Properties Expressible with Four First-Order Quantifiers. LATA 2010: 333-343

Charles Jordan, Thomas Zeugmann: A Note on the Testability of Ramsey's Class. TAMC 2010: 296-307

Thomas Zeugmann: Epsilon Covers. Encyclopedia of Machine Learning 2010: 326

Thomas Zeugmann: Epsilon Nets. Encyclopedia of Machine Learning 2010: 326-327

Thomas Zeugmann: PAC Learning. Encyclopedia of Machine Learning 2010: 745-753

Thomas Zeugmann: Stochastic Finite Learning. Encyclopedia of Machine Learning 2010: 925-928

Thomas Zeugmann: VC Dimension. Encyclopedia of Machine Learning 2010: 1021-1024

Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann (Eds.): Algorithmic Learning Theory, 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings. Lecture Notes in Computer Science 6331, Springer 2010, isbn 978-3-642-16107-0

Frank J. Balbach, Thomas Zeugmann: Recent Developments in Algorithmic Teaching. LATA 2009: 1-18

Charles Jordan, Thomas Zeugmann: Relational Properties Expressible with One Universal Quantifier Are Testable. SAGA 2009: 141-155

Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, Sandra Zilles (Eds.): Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings. Lecture Notes in Computer Science 5809, Springer 2009, isbn 978-3-642-04413-7

Osamu Watanabe, Thomas Zeugmann (Eds.): Stochastic Algorithms: Foundations and Applications, 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings. Lecture Notes in Computer Science 5792, Springer 2009, isbn 978-3-642-04943-9

Yohji Akama, Thomas Zeugmann: Consistent and coherent learning with delta-delay. Inf. Comput. 206(11): 1362-1374 (2008)

John Case, Takeshi Shinohara, Thomas Zeugmann, Sandra Zilles: Foreword. Theor. Comput. Sci. 397(1-3): 1-3 (2008)

Thomas Zeugmann, Sandra Zilles: Learning recursive functions: A survey. Theor. Comput. Sci. 397(1-3): 4-56 (2008)

Steffen Lange, Thomas Zeugmann, Sandra Zilles: Learning indexed families of recursive languages from positive data: A survey. Theor. Comput. Sci. 397(1-3): 194-232 (2008)

Skip Jordan, Thomas Zeugmann: Indistinguishability and First-Order Logic. TAMC 2008: 94-104

Yoav Freund, László Györfi, György Turán, Thomas Zeugmann (Eds.): Algorithmic Learning Theory, 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings. Lecture Notes in Computer Science 5254, Springer 2008, isbn 978-3-540-87986-2

Shai Ben-David, John Case, Thomas Zeugmann: Foreword. Theor. Comput. Sci. 382(3): 167-169 (2007)

Nicolò Cesa-Bianchi, Rüdiger Reischuk, Thomas Zeugmann: Foreword. Theor. Comput. Sci. 350(1): 1-2 (2006)

Thomas Zeugmann: From learning in the limit to stochastic finite learning. Theor. Comput. Sci. 364(1): 77-97 (2006)

John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann: Learning a subclass of regular patterns in polynomial time. Theor. Comput. Sci. 364(1): 115-131 (2006)

Frank J. Balbach, Thomas Zeugmann: Teaching Memoryless Randomized Learners Without Feedback. ALT 2006: 93-108

Frank J. Balbach, Thomas Zeugmann: Teaching Randomized Learners. COLT 2006: 229-243

Frank J. Balbach, Thomas Zeugmann: On the Teachability of Randomized Learners. Complexity of Boolean Functions 2006

Jan Poland, Thomas Zeugmann: Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts. Discovery Science 2006: 197-208

Yohji Akama, Thomas Zeugmann: Consistency Conditions for Inductive Inference of Recursive Functions. JSAI 2006: 251-264

Ryutaro Kurai, Shin-ichi Minato, Thomas Zeugmann: N-Gram Analysis Based on Zero-Suppressed BDDs. JSAI 2006: 289-300

Thomas Zeugmann: Inductive Inference and Language Learning. TAMC 2006: 464-473

Steffen Lange, Gunter Grieser, Thomas Zeugmann: Inductive inference of approximations for recursive concepts. Theor. Comput. Sci. 348(1): 15-40 (2005)

Frank J. Balbach, Thomas Zeugmann: Teaching Learners with Restricted Mind Changes. ALT 2005: 474-489

Björn Hoffmeister, Thomas Zeugmann: Text Mining Using Markov Chains of Variable Length. Federation over the Web 2005: 1-24

John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann: A Polynomial Time Learner for a Subclass of Regular Patterns. Electronic Colloquium on Computational Complexity (ECCC)(038) (2004)

Sanjay Jain, Efim B. Kinber, Rolf Wiehagen, Thomas Zeugmann: On learning of functions refutably. Theor. Comput. Sci. 1(298): 111-143 (2003)

Thomas Zeugmann: Can Learning in the Limit Be Done Efficiently? ALT 2003: 17-38

John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann: Learning a Subclass of Regular Patterns in Polynomial Time. ALT 2003: 234-246

Thomas Zeugmann: Can Learning in the Limit Be Done Efficiently? Discovery Science 2003: 46

Frank Stephan, Thomas Zeugmann: Learning classes of approximations to non-recursive function. Theor. Comput. Sci. 288(2): 309-341 (2002)

Peter Rossmanith, Thomas Zeugmann: Stochastic Finite Learning of the Pattern Languages. Machine Learning 44(1/2): 67-91 (2001)

Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, Thomas Zeugmann: Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries. Theor. Comput. Sci. 261(1): 119-156 (2001)

Rolf Wiehagen, Thomas Zeugmann: Foreword. Theor. Comput. Sci. 268(2): 175-177 (2001)

Naoki Abe, Roni Khardon, Thomas Zeugmann: Editors' Introduction. ALT 2001: 1-8

Sanjay Jain, Efim B. Kinber, Rolf Wiehagen, Thomas Zeugmann: Learning Recursive Functions Refutably. ALT 2001: 283-298

Thomas Zeugmann: Stochastic Finite Learning. SAGA 2001: 155-172

Naoki Abe, Roni Khardon, Thomas Zeugmann (Eds.): Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings. Lecture Notes in Computer Science 2225, Springer 2001, isbn 3-540-42875-5

Rüdiger Reischuk, Thomas Zeugmann: An Average-Case Optimal One-Variable Pattern Language Learner. J. Comput. Syst. Sci. 60(2): 302-335 (2000)

Sanjay Jain, Efim B. Kinber, Steffen Lange, Rolf Wiehagen, Thomas Zeugmann: Learning languages and functions by erasing. Theor. Comput. Sci. 241(1-2): 143-189 (2000)

Gunter Grieser, Steffen Lange, Thomas Zeugmann: Learning Recursive Concepts with Anomalies. ALT 2000: 101-115

Frank Stephan, Thomas Zeugmann: Average-Case Complexity of Learning Polynomials. COLT 2000: 59-68

John Case, Sanjay Jain, Steffen Lange, Thomas Zeugmann: Incremental Concept Learning for Bounded Data Mining. Inf. Comput. 152(1): 74-110 (1999)

Frank Stephan, Thomas Zeugmann: On the Uniform Learnability of Approximations to Non-Recursive Functions. ALT 1999: 276-290

Rüdiger Reischuk, Thomas Zeugmann: A Complete and Tight Average-Case Analysis of Learning Monomials. STACS 1999: 414-423

Thomas Zeugmann: Lange and Wiehagen's Pattern Language Learning Algorithm: An Average-Case Analysis with Respect to its Total Learning Time. Ann. Math. Artif. Intell. 23(1-2): 117-145 (1998)

Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann: Editor's Introduction. ALT 1998: 1-10

Rüdiger Reischuk, Thomas Zeugmann: Learning One-Variable Pattern Languages in Linear Average Time. COLT 1998: 198-208

Peter Rossmanith, Thomas Zeugmann: Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data. ICGI 1998: 13-24

Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann (Eds.): Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 8-10, 1998, Proceedings. Lecture Notes in Computer Science 1501, Springer 1998, isbn 3-540-65013-X

Rüdiger Reischuk, Thomas Zeugmann: An Average-Case Optimal One-Variable Pattern Language Learner. Electronic Colloquium on Computational Complexity (ECCC) 5(69) (1998)

Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann: Classifying Predicates and Languages. Int. J. Found. Comput. Sci. 8(1): 15-42 (1997)

Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, Thomas Zeugmann: Learning One-Variable Pattern Languages Very Efficiently on Average, in Parallel, and by Asking Queries. ALT 1997: 260-276

Steffen Lange, Thomas Zeugmann: Incremental Learning from Positive Data. J. Comput. Syst. Sci. 53(1): 88-103 (1996)

Steffen Lange, Thomas Zeugmann: Set-Driven and Rearrangement-Independent Learning of Recursive Languages. Mathematical Systems Theory 29(6): 599-634 (1996)

Steffen Lange, Thomas Zeugmann, Shyam Kapur: Monotonic and Dual Monotonic Language Learning. Theor. Comput. Sci. 155(2): 365-410 (1996)

Steffen Lange, Rolf Wiehagen, Thomas Zeugmann: Learning by Erasing. ALT 1996: 228-241

Rusins Freivalds, Thomas Zeugmann: Co-Learning of Recursive Languages from Positive Data. Ershov Memorial Conference 1996: 122-133

William I. Gasarch, Efim B. Kinber, Mark G. Pleszkoch, Carl H. Smith, Thomas Zeugmann: Learning via Queries with Teams and Anomalies. Fundam. Inform. 23(1): 67-89 (1995)

Thomas Zeugmann, Steffen Lange, Shyam Kapur: Characterizations of Monotonic and Dual Monotonic Language Learning. Inf. Comput. 120(2): 155-173 (1995)

Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann: Editor's Introduction. ALT 1995: ix-xv

Steffen Lange, Thomas Zeugmann: Trading monotonicity demands versus mind changes. EuroCOLT 1995: 125-139

Rolf Wiehagen, Thomas Zeugmann: Learning and Consistency. GOSLER Final Report 1995: 1-24

Rolf Wiehagen, Carl H. Smith, Thomas Zeugmann: Classifying Recursive Predicates and Languages. GOSLER Final Report 1995: 174-189

Thomas Zeugmann, Steffen Lange: A Guided Tour Across the Boundaries of Learning Recursive Languages. GOSLER Final Report 1995: 190-258

Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann (Eds.): Algorithmic Learning Theory, 6th International Conference, ALT '95, Fukuoka, Japan, October 18-20, 1995, Proceedings. Lecture Notes in Computer Science 997, Springer 1995, isbn 3-540-60454-5

Steffen Lange, Thomas Zeugmann: Characterization of language learning front informant under various monotonicity constraints. J. Exp. Theor. Artif. Intell. 6(1): 73-94 (1994)

Rolf Wiehagen, Thomas Zeugmann: Ignoring data may be the only way to learn efficiently. J. Exp. Theor. Artif. Intell. 6(1): 131-144 (1994)

Thomas Zeugmann: Report on COLT 1994. SIGACT News 25(4): 88-95 (1994)

Thomas Zeugmann: Average Case Analysis of Pattern Language Learning Algorithms (Abstract). AII/ALT 1994: 8-9

Steffen Lange, Thomas Zeugmann: Set-Driven and Rearrangement-Independent Learning of Recursive Languages. AII/ALT 1994: 453-468

Steffen Lange, Thomas Zeugmann: Learning Recursive Languages with Bounded Mind Changes. Int. J. Found. Comput. Sci. 4(2): 157-178 (1993)

Steffen Lange, Thomas Zeugmann: Language Learning in Dependence on the Space of Hypotheses. COLT 1993: 127-136

Steffen Lange, Thomas Zeugmann: Language Learning with a Bounded Number of Mind Changes. STACS 1993: 682-691

Thomas Zeugmann: Highly Parallel Computations Modulo a Number Having Only Small Prime Factors. Inf. Comput. 96(1): 95-114 (1992)

Rolf Wiehagen, Thomas Zeugmann: Too Much Can be Too Much for Learning Efficiently. AII 1992: 72-86

Steffen Lange, Thomas Zeugmann: A Unifying Approach to Monotonic Language Learning on Informant. AII 1992: 244-259

Steffen Lange, Thomas Zeugmann: Types of Monotonic Language Learning and Their Characterization. COLT 1992: 377-390

Efim B. Kinber, Thomas Zeugmann: One-Sided Error Probabilistic Inductive Inference and Reliable Frequency Identification. Inf. Comput. 92(2): 253-284 (1991)

Steffen Lange, Thomas Zeugmann: Monotonic Versus Nonmonotonic Language Learning. Nonmonotonic and Inductive Logic 1991: 254-269

Efim B. Kinber, William I. Gasarch, Thomas Zeugmann, Mark G. Pleszkoch, Carl H. Smith: Learning Via Queries With Teams and Anomilies. COLT 1990: 327-337

Thomas Zeugmann: Computing Large Polynomial Powers Very Fast in Parallel. MFCS 1990: 538-544

Thomas Zeugmann: Inductive Inference of Optimal Programs: A Survey and Open Problems. Nonmonotonic and Inductive Logic 1990: 208-222

Thomas Zeugmann: Improved Parallel Computations in the Ring Z/p^alpha. Elektronische Informationsverarbeitung und Kybernetik 25(10): 543-547 (1989)

Efim B. Kinber, Thomas Zeugmann: Refined Query Inference (Extended Abstract). AII 1989: 148-160

Efim B. Kinber, Thomas Zeugmann: Monte-Carlo Inference and Its Relations to Reliable Frequency Identification. FCT 1989: 257-266

Thomas Zeugmann: On the Power of Recursive Optimizers. Theor. Comput. Sci. 62(3): 289-310 (1988)

Thomas Zeugmann: On Barzdin's Conjecture. AII 1986: 220-227

Efim B. Kinber, Thomas Zeugmann: Inductive Inference of Almost Everywhere Correct Programs by Reliably Working Strategies. Elektronische Informationsverarbeitung und Kybernetik 21(3): 91-100 (1985)

Thomas Zeugmann: On recursive optimizers. Mathematical Methods of Specification and Synthesis of Software Systems 1985: 240-245

Thomas Zeugmann: A-posteriori Characterizations in Inductive Inference of Recursive Functions. Elektronische Informationsverarbeitung und Kybernetik 19(10/11): 559-594 (1983)

Thomas Zeugmann: On the Synthesis of Fastest Programs in Inductive Inference. Elektronische Informationsverarbeitung und Kybernetik 19(12): 625-642 (1983)