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2020 – today
- 2021
[j49]Sanjay Jain, Frank Stephan, Thomas Zeugmann:
On the amount of nonconstructivity in learning formal languages from text. Inf. Comput. 281: 104668 (2021)- 2020
- [c66]Thomas Zeugmann:
On the Interplay Between Inductive Inference of Recursive Functions, Complexity Theory and Recursive Numberings. CiE 2020: 124-136
2010 – 2019
- 2018
- [j48]Thomas Zeugmann:
Guest Editor's Foreword. Theor. Comput. Sci. 733: 1-3 (2018) - [c65]Ziyuan Gao, Sanjay Jain, Frank Stephan, Thomas Zeugmann:
On the Help of Bounded Shot Verifiers, Comparators and Standardisers for Learnability in Inductive Inference. ALT 2018: 413-437 - 2017
- [r10]Thomas Zeugmann:
Epsilon Cover. Encyclopedia of Machine Learning and Data Mining 2017: 408-409 - [r9]Thomas Zeugmann:
Epsilon Nets. Encyclopedia of Machine Learning and Data Mining 2017: 409-410 - [r8]Thomas Zeugmann:
PAC Learning. Encyclopedia of Machine Learning and Data Mining 2017: 949-959 - [r7]Thomas Zeugmann:
Stochastic Finite Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1187-1191 - [r6]Thomas Zeugmann:
VC Dimension. Encyclopedia of Machine Learning and Data Mining 2017: 1323-1327 - 2016
- [b2]Werner Römisch, Thomas Zeugmann:
Mathematical Analysis and the Mathematics of Computation. Springer 2016, ISBN 978-3-319-42753-9, pp. 1-703 - [j47]Charles Jordan, Thomas Zeugmann:
The Kahr-Moore-Wang Class Contains Untestable Properties. Balt. J. Mod. Comput. 4(4) (2016) - [j46]Thomas Zeugmann:
Obituary Rūsiņš Mārtiņš Freivalds (1942-2016). Bull. EATCS 118 (2016) - [j45]Sanjay Jain, Rémi Munos, Frank Stephan
, Thomas Zeugmann:
Guest Editors' foreword. Theor. Comput. Sci. 620: 1-3 (2016) - [j44]Peter Auer, Alexander Clark, Thomas Zeugmann:
Guest editors' foreword. Theor. Comput. Sci. 650: 1-3 (2016) - 2015
- [c64]Yu Zhu, Thomas Zeugmann:
Image Analysis in a Parameter-Free Setting. ISCIS 2015: 285-294 - 2014
- [j43]Ilja Kucevalovs, Ojars Krasts, Rusins Freivalds, Thomas Zeugmann:
On the Influence of Technology on Learning Processes. Parallel Process. Lett. 24(2) (2014) - [j42]Jyrki Kivinen, Csaba Szepesvári, Thomas Zeugmann:
Guest Editors' introduction. Theor. Comput. Sci. 519: 1-3 (2014) - [j41]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Guest Editors' foreword. Theor. Comput. Sci. 558: 1-4 (2014) - [c63]Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles:
Editors' Introduction. ALT 2014: 1-7 - [c62]Rusins Freivalds, Thomas Zeugmann:
Active Learning of Recursive Functions by Ultrametric Algorithms. SOFSEM 2014: 246-257 - [e11]Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles:
Algorithmic Learning Theory - 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. Lecture Notes in Computer Science 8776, Springer 2014, ISBN 978-3-319-11661-7 [contents] - 2013
- [j40]Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann:
Guest Editors' foreword. Theor. Comput. Sci. 473: 1-3 (2013) - [c61]Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann:
Editors' Introduction. ALT 2013: 1-12 - [c60]Rusins Freivalds, Thomas Zeugmann, Grant R. Pogosyan:
On the Size Complexity of Deterministic Frequency Automata. LATA 2013: 287-298 - [e10]Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann:
Algorithmic Learning Theory - 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings. Lecture Notes in Computer Science 8139, Springer 2013, ISBN 978-3-642-40934-9 [contents] - 2012
- [j39]Charles Jordan, Thomas Zeugmann:
Testable and untestable classes of first-order formulae. J. Comput. Syst. Sci. 78(5): 1557-1578 (2012) - [c59]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Editors' Introduction. ALT 2012: 1-11 - [c58]Sanjay Jain, Frank Stephan, Thomas Zeugmann:
On the Amount of Nonconstructivity in Learning Formal Languages from Positive Data. TAMC 2012: 423-434 - [e9]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
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 [contents] - 2011
- [j38]Frank J. Balbach, Thomas Zeugmann:
Teaching randomized learners with feedback. Inf. Comput. 209(3): 296-319 (2011) - [c57]Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann:
Editors' Introduction. ALT 2011: 1-13 - [c56]Frank Stephan, Ryo Yoshinaka, Thomas Zeugmann:
On the Parameterised Complexity of Learning Patterns. ISCIS 2011: 277-281 - [c55]Rusins Freivalds, Thomas Zeugmann:
On the Amount of Nonconstructivity in Learning Recursive Functions. TAMC 2011: 332-343 - [c54]Charles Jordan, Thomas Zeugmann:
Untestable Properties in the Kahr-Moore-Wang Class. WoLLIC 2011: 176-186 - [e8]Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann:
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 [contents] - 2010
- [j37]László Györfi, György Turán, Thomas Zeugmann:
Guest editors' foreword. Theor. Comput. Sci. 411(29-30): 2629-2631 (2010) - [c53]Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann:
Editors' Introduction. ALT 2010: 1-10 - [c52]Kimihito Ito, Thomas Zeugmann, Yu Zhu:
Clustering the Normalized Compression Distance for Influenza Virus Data. Algorithms and Applications 2010: 130-146 - [c51]Kimihito Ito, Thomas Zeugmann, Yu Zhu:
Recent Experiences in Parameter-Free Data Mining. ISCIS 2010: 365-371 - [c50]Charles Jordan, Thomas Zeugmann:
Untestable Properties Expressible with Four First-Order Quantifiers. LATA 2010: 333-343 - [c49]Charles Jordan, Thomas Zeugmann:
A Note on the Testability of Ramsey's Class. TAMC 2010: 296-307 - [e7]Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann:
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 [contents] - [r5]Thomas Zeugmann:
Epsilon Covers. Encyclopedia of Machine Learning 2010: 326 - [r4]Thomas Zeugmann:
Epsilon Nets. Encyclopedia of Machine Learning 2010: 326-327 - [r3]Thomas Zeugmann:
PAC Learning. Encyclopedia of Machine Learning 2010: 745-753 - [r2]Thomas Zeugmann:
Stochastic Finite Learning. Encyclopedia of Machine Learning 2010: 925-928 - [r1]Thomas Zeugmann:
VC Dimension. Encyclopedia of Machine Learning 2010: 1021-1024
2000 – 2009
- 2009
- [c48]Frank J. Balbach, Thomas Zeugmann:
Recent Developments in Algorithmic Teaching. LATA 2009: 1-18 - [c47]Charles Jordan, Thomas Zeugmann:
Relational Properties Expressible with One Universal Quantifier Are Testable. SAGA 2009: 141-155 - [e6]Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, Sandra Zilles:
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 [contents] - [e5]Osamu Watanabe, Thomas Zeugmann:
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 [contents] - 2008
- [j36]Yohji Akama, Thomas Zeugmann:
Consistent and coherent learning with delta-delay. Inf. Comput. 206(11): 1362-1374 (2008) - [j35]John Case, Takeshi Shinohara, Thomas Zeugmann, Sandra Zilles:
Foreword. Theor. Comput. Sci. 397(1-3): 1-3 (2008) - [j34]Thomas Zeugmann, Sandra Zilles:
Learning recursive functions: A survey. Theor. Comput. Sci. 397(1-3): 4-56 (2008) - [j33]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) - [c46]Skip Jordan, Thomas Zeugmann:
Indistinguishability and First-Order Logic. TAMC 2008: 94-104 - [e4]Yoav Freund, László Györfi, György Turán, Thomas Zeugmann:
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 [contents] - 2007
- [j32]Shai Ben-David, John Case, Thomas Zeugmann:
Foreword. Theor. Comput. Sci. 382(3): 167-169 (2007) - 2006
- [j31]Nicolò Cesa-Bianchi, Rüdiger Reischuk, Thomas Zeugmann:
Foreword. Theor. Comput. Sci. 350(1): 1-2 (2006) - [j30]Thomas Zeugmann:
From learning in the limit to stochastic finite learning. Theor. Comput. Sci. 364(1): 77-97 (2006) - [j29]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) - [c45]Frank J. Balbach, Thomas Zeugmann:
Teaching Memoryless Randomized Learners Without Feedback. ALT 2006: 93-108 - [c44]Frank J. Balbach, Thomas Zeugmann:
Teaching Randomized Learners. COLT 2006: 229-243 - [c43]Jan Poland, Thomas Zeugmann:
Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts. Discovery Science 2006: 197-208 - [c42]Yohji Akama, Thomas Zeugmann:
Consistency Conditions for Inductive Inference of Recursive Functions. JSAI 2006: 251-264 - [c41]Ryutaro Kurai, Shin-ichi Minato, Thomas Zeugmann:
N-Gram Analysis Based on Zero-Suppressed BDDs. JSAI 2006: 289-300 - [c40]Thomas Zeugmann:
Inductive Inference and Language Learning. TAMC 2006: 464-473 - [i3]Frank J. Balbach, Thomas Zeugmann:
On the Teachability of Randomized Learners. Complexity of Boolean Functions 2006 - 2005
- [j28]Steffen Lange, Gunter Grieser, Thomas Zeugmann:
Inductive inference of approximations for recursive concepts. Theor. Comput. Sci. 348(1): 15-40 (2005) - [c39]Frank J. Balbach, Thomas Zeugmann:
Teaching Learners with Restricted Mind Changes. ALT 2005: 474-489 - [c38]Björn Hoffmeister, Thomas Zeugmann:
Text Mining Using Markov Chains of Variable Length. Federation over the Web 2005: 1-24 - 2004
- [i2]John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann:
A Polynomial Time Learner for a Subclass of Regular Patterns. Electron. Colloquium Comput. Complex. TR04 (2004) - 2003
- [j27]Sanjay Jain, Efim B. Kinber, Rolf Wiehagen, Thomas Zeugmann:
On learning of functions refutably. Theor. Comput. Sci. 298(1): 111-143 (2003) - [c37]Thomas Zeugmann:
Can Learning in the Limit Be Done Efficiently? ALT 2003: 17-38 - [c36]John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann:
Learning a Subclass of Regular Patterns in Polynomial Time. ALT 2003: 234-246 - [c35]Thomas Zeugmann:
Can Learning in the Limit Be Done Efficiently? Discovery Science 2003: 46 - 2002
- [j26]Frank Stephan, Thomas Zeugmann:
Learning classes of approximations to non-recursive function. Theor. Comput. Sci. 288(2): 309-341 (2002) - 2001
- [j25]Peter Rossmanith, Thomas Zeugmann:
Stochastic Finite Learning of the Pattern Languages. Mach. Learn. 44(1/2): 67-91 (2001) - [j24]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) - [j23]Rolf Wiehagen, Thomas Zeugmann:
Foreword. Theor. Comput. Sci. 268(2): 175-177 (2001) - [c34]Naoki Abe, Roni Khardon, Thomas Zeugmann:
Editors' Introduction. ALT 2001: 1-8 - [c33]Sanjay Jain, Efim B. Kinber, Rolf Wiehagen, Thomas Zeugmann:
Learning Recursive Functions Refutably. ALT 2001: 283-298 - [c32]Thomas Zeugmann:
Stochastic Finite Learning. SAGA 2001: 155-172 - [e3]Naoki Abe, Roni Khardon, Thomas Zeugmann:
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 [contents] - 2000
- [j22]Rüdiger Reischuk, Thomas Zeugmann:
An Average-Case Optimal One-Variable Pattern Language Learner. J. Comput. Syst. Sci. 60(2): 302-335 (2000) - [j21]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) - [c31]Gunter Grieser, Steffen Lange, Thomas Zeugmann:
Learning Recursive Concepts with Anomalies. ALT 2000: 101-115 - [c30]Frank Stephan, Thomas Zeugmann:
Average-Case Complexity of Learning Polynomials. COLT 2000: 59-68
1990 – 1999
- 1999
- [j20]John Case, Sanjay Jain, Steffen Lange, Thomas Zeugmann:
Incremental Concept Learning for Bounded Data Mining. Inf. Comput. 152(1): 74-110 (1999) - [c29]Frank Stephan, Thomas Zeugmann:
On the Uniform Learnability of Approximations to Non-Recursive Functions. ALT 1999: 276-290 - [c28]Rüdiger Reischuk, Thomas Zeugmann:
A Complete and Tight Average-Case Analysis of Learning Monomials. STACS 1999: 414-423 - 1998
- [j19]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) - [c27]Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann:
Editor's Introduction. ALT 1998: 1-10 - [c26]Rüdiger Reischuk, Thomas Zeugmann:
Learning One-Variable Pattern Languages in Linear Average Time. COLT 1998: 198-208 - [c25]Peter Rossmanith, Thomas Zeugmann:
Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data. ICGI 1998: 13-24 - [e2]Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann:
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 [contents] - [i1]Rüdiger Reischuk, Thomas Zeugmann:
An Average-Case Optimal One-Variable Pattern Language Learner. Electron. Colloquium Comput. Complex. TR98 (1998) - 1997
- [j18]Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann:
Classifying Predicates and Languages. Int. J. Found. Comput. Sci. 8(1): 15-42 (1997) - [c24]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 - 1996
- [j17]Steffen Lange, Thomas Zeugmann:
Incremental Learning from Positive Data. J. Comput. Syst. Sci. 53(1): 88-103 (1996) - [j16]Steffen Lange, Thomas Zeugmann:
Set-Driven and Rearrangement-Independent Learning of Recursive Languages. Math. Syst. Theory 29(6): 599-634 (1996) - [j15]Steffen Lange, Thomas Zeugmann, Shyam Kapur:
Monotonic and Dual Monotonic Language Learning. Theor. Comput. Sci. 155(2): 365-410 (1996) - [c23]Steffen Lange, Rolf Wiehagen, Thomas Zeugmann:
Learning by Erasing. ALT 1996: 228-241 - [c22]Rusins Freivalds, Thomas Zeugmann:
Co-Learning of Recursive Languages from Positive Data. Ershov Memorial Conference 1996: 122-133 - 1995
- [j14]William I. Gasarch, Efim B. Kinber, Mark G. Pleszkoch, Carl H. Smith, Thomas Zeugmann:
Learning via Queries with Teams and Anomalies. Fundam. Informaticae 23(1): 67-89 (1995) - [j13]Thomas Zeugmann, Steffen Lange, Shyam Kapur:
Characterizations of Monotonic and Dual Monotonic Language Learning. Inf. Comput. 120(2): 155-173 (1995) - [c21]Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann:
Editor's Introduction. ALT 1995: ix-xv - [c20]Steffen Lange, Thomas Zeugmann:
Trading monotonicity demands versus mind changes. EuroCOLT 1995: 125-139 - [c19]Rolf Wiehagen, Thomas Zeugmann:
Learning and Consistency. GOSLER Final Report 1995: 1-24 - [c18]Rolf Wiehagen, Carl H. Smith, Thomas Zeugmann:
Classifying Recursive Predicates and Languages. GOSLER Final Report 1995: 174-189 - [c17]Thomas Zeugmann, Steffen Lange:
A Guided Tour Across the Boundaries of Learning Recursive Languages. GOSLER Final Report 1995: 190-258 - [e1]Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann:
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 [contents] - 1994
- [j12]Steffen Lange, Thomas Zeugmann:
Characterization of language learning front informant under various monotonicity constraints. J. Exp. Theor. Artif. Intell. 6(1): 73-94 (1994) - [j11]Rolf Wiehagen, Thomas Zeugmann:
Ignoring data may be the only way to learn efficiently. J. Exp. Theor. Artif. Intell. 6(1): 131-144 (1994) - [j10]Thomas Zeugmann:
Report on COLT 1994. SIGACT News 25(4): 88-95 (1994) - [c16]Thomas Zeugmann:
Average Case Analysis of Pattern Language Learning Algorithms (Abstract). AII/ALT 1994: 8-9 - [c15]Steffen Lange, Thomas Zeugmann:
Set-Driven and Rearrangement-Independent Learning of Recursive Languages. AII/ALT 1994: 453-468 - 1993
- [j9]Steffen Lange, Thomas Zeugmann:
Learning Recursive Languages with Bounded Mind Changes. Int. J. Found. Comput. Sci. 4(2): 157-178 (1993) - [c14]Steffen Lange, Thomas Zeugmann:
Language Learning in Dependence on the Space of Hypotheses. COLT 1993: 127-136 - [c13]Rolf Wiehagen, Carl H. Smith, Thomas Zeugmann:
Classification of predicates and languages. EuroCOLT 1993: 171-181 - [c12]Steffen Lange, Thomas Zeugmann:
Language Learning with a Bounded Number of Mind Changes. STACS 1993: 682-691 - 1992
- [j8]Thomas Zeugmann:
Highly Parallel Computations Modulo a Number Having Only Small Prime Factors. Inf. Comput. 96(1): 95-114 (1992) - [c11]Rolf Wiehagen, Thomas Zeugmann:
Too Much Can be Too Much for Learning Efficiently. AII 1992: 72-86 - [c10]Steffen Lange, Thomas Zeugmann:
A Unifying Approach to Monotonic Language Learning on Informant. AII 1992: 244-259