Thomas Zeugmann

发表

Learning a subclass of regular patterns in polynomial time

John Case, Sanjay Jain, Rüdiger Reischuk, 2006, Theor. Comput. Sci..

Continuous Functions in Metric Spaces

Thomas Zeugmann, Werner Römisch, 2016 .

Types of monotonic language learning and their characterization

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1992, COLT '92.

Approximative Representation of Functions

Thomas Zeugmann, Werner Römisch, 2016 .

Image Analysis in a Parameter-Free Setting

Thomas Zeugmann, Yu Zhu, T. Zeugmann, 2015, ISCIS.

F-035 Detecting Natural Similarities in Scientific Documents : Author versus Content

Thomas Zeugmann, 宇野友洋, 2010 .

VC Dimension

Thomas Zeugmann, 2010, Encyclopedia of Machine Learning.

Teaching randomized learners with feedback

Thomas Zeugmann, Frank J. Balbach, T. Zeugmann, 2011, Inf. Comput..

Learning Recursive Concepts with Anomalies

Thomas Zeugmann, Steffen Lange, Gunter Grieser, 2000, ALT.

Set-Driven and Rearrangement-Independent Learning of Recursive Languages

Thomas Zeugmann, Steffen Lange, 1994, AII/ALT.

Teaching Memoryless Randomized Learners Without Feedback

Thomas Zeugmann, Frank J. Balbach, 2006, ALT.

Text Mining Using Markov Chains of Variable Length

Björn Hoffmeister, Thomas Zeugmann, T. Zeugmann, 2005, Federation over the Web.

Average-Case Complexity of Learning Polynomials

Frank Stephan, Thomas Zeugmann, 2000, COLT.

PAC Learning

Thomas Zeugmann, 2010, Encyclopedia of Machine Learning.

Stochastic Finite Learning of the Pattern Languages

Thomas Zeugmann, Peter Rossmanith, T. Zeugmann, 2001, Machine Learning.

Lange and Wiehagen's pattern language learning algorithm: An average-case analysis with respect to its total learning time

Thomas Zeugmann, T. Zeugmann, 1995, Annals of Mathematics and Artificial Intelligence.

Stochastic Finite Learning

Thomas Zeugmann, 2001, SAGA.

Learning one-variable pattern languages in linear average time

Rüdiger Reischuk, Thomas Zeugmann, R. Reischuk, 1997, COLT' 98.

Incremental Learning from Positive Data

Thomas Zeugmann, Steffen Lange, 1996, J. Comput. Syst. Sci..

Guest editors' foreword

László Györfi, Thomas Zeugmann, György Turán, 2010, Theoretical Computer Science.

Highly Parallel Computations Modulo a Number Having Only Small Prime Factors

Thomas Zeugmann, 1992, Inf. Comput..

Learning indexed families of recursive languages from positive data: A survey

Thomas Zeugmann, Sandra Zilles, Steffen Lange, 2008, Theor. Comput. Sci..

Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications

Osamu Watanabe, Thomas Zeugmann, 2009 .

Report on COLT 1994

Thomas Zeugmann, 1994, SIGA.

ALT 2008. 19th international conference on algorithmic learning theory. Budapest, 2008. (Lecture notes in artificial intelligence 5254.)

Yoav Freund, László Györfi, Thomas Zeugmann, 2008 .

Learning via queries with teams and anomilies

Thomas Zeugmann, Carl Smith, Mark G. Pleszkoch, 1990, COLT '90.

Process-Based Modeling

Stan Matwin, Eyke Hüllermeier, Johannes Fürnkranz, 2010, Encyclopedia of Machine Learning.

Monotonic and Dual Monotonic Language Learning

Thomas Zeugmann, Shyam Kapur, Steffen Lange, 1996, Theor. Comput. Sci..

Two Variations of Inductive Inference of Languages from Positive Data

Thomas Zeugmann, Takashi Tabe, 田部尚志, 1995 .

Learning recursive functions: A survey

Thomas Zeugmann, Sandra Zilles, T. Zeugmann, 2008, Theor. Comput. Sci..

On the amount of nonconstructivity in learning formal languages from text

Frank Stephan, Thomas Zeugmann, Sanjay Jain, 2020 .

Learning classes of approximations to non-recursive function

Frank Stephan, Thomas Zeugmann, T. Zeugmann, 2002, Theor. Comput. Sci..

Monotonic Versus Nonmonotonic Language Learning

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1991, Nonmonotonic and Inductive Logic.

Monte-Carlo Inference and Its Relations to Reliable Frequency Identification

Thomas Zeugmann, Efim B. Kinber, 1989, FCT.

Editors' Introduction

Thomas Zeugmann, Csaba Szepesvári, Esko Ukkonen, 2011, ALT.

On the Help of Bounded Shot Verifiers, Comparators and Standardisers for Learnability in Inductive Inference

Sanjay Jain, Frank Stephan, Thomas Zeugmann, 2018, ALT.

Epsilon Covers

Thomas Zeugmann, 2010, Encyclopedia of Machine Learning.

Editors' Introduction

Sanjay Jain, Frank Stephan, Thomas Zeugmann, 1997, First Monday.

Stochastic Finite Learning

Thomas Zeugmann, 2010, Encyclopedia of Machine Learning.

Algorithmic Learning Theory, 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings

Frank Stephan, Thomas Zeugmann, Vladimir Vovk, 2010, ALT.

Improved Parallel Computations in the Ring Z/palpha

Thomas Zeugmann, 1989, J. Inf. Process. Cybern..

Proceedings of the 24th International Conference on Algorithmic Learning Theory - Volume 8139

Frank Stephan, Thomas Zeugmann, Sanjay Jain, 2013 .

Stochastic Finite Learning

Thomas Zeugmann, 2017, Encyclopedia of Machine Learning and Data Mining.

On recursive optimizers

Thomas Zeugmann, 1985, Mathematical Methods of Specification and Synthesis of Software Systems.

A Polynomial Time Learner for a Subclass of Regular Patterns

John Case, Sanjay Jain, Rüdiger Reischuk, 2004, Electron. Colloquium Comput. Complex..

Can Learning in the Limit Be Done Efficiently?

Thomas Zeugmann, 2003, Discovery Science.

Recent Developments in Algorithmic Teaching

Thomas Zeugmann, Frank J. Balbach, T. Zeugmann, 2009, LATA.

Editors' Introduction

Naoki Abe, Thomas Zeugmann, Roni Khardon, 2001, International Conference on Algorithmic Learning Theory.

Relational Properties Expressible with One Universal Quantifier Are Testable

Thomas Zeugmann, Charles Jordan, T. Zeugmann, 2009, SAGA.

Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts

Thomas Zeugmann, Jan Poland, T. Zeugmann, 2006, Discovery Science.

Learning by Erasing

Rolf Wiehagen, Thomas Zeugmann, Steffen Lange, 1996, ALT.

Proceedings of the 22nd international conference on Algorithmic learning theory

Thomas Zeugmann, Csaba Szepesvári, Esko Ukkonen, 1995 .

Inductive Inference of Almost Everywhere Correct Programs by Reliably Working Strategies

Thomas Zeugmann, Efim B. Kinber, T. Zeugmann, 1985, J. Inf. Process. Cybern..

Analyzing the Average-Case Behavior of Conjunctive Learning Algorithms

Rüdiger Reischuk, Thomas Zeugmann, 1998 .

Refined Query Inference (Extended Abstract)

Thomas Zeugmann, Efim B. Kinber, 1989, AII.

Guest Editor's Foreword

Thomas Zeugmann, 2018, Theor. Comput. Sci..

Guest Editors' foreword

Sanjay Jain, Frank Stephan, Thomas Zeugmann, 2016, Theor. Comput. Sci..

A Note on the Testability of Ramsey's Class

Thomas Zeugmann, Charles Jordan, 2010, TAMC.

A Guided Tour Across the Boundaries of Learning Recursive Languages

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1995, GOSLER Final Report.

Untestable Properties Expressible with Four First-Order Quantifiers

Thomas Zeugmann, Charles Jordan, 2010, LATA.

Untestable Properties in the Kahr-Moore-Wang Class

Thomas Zeugmann, Charles Jordan, T. Zeugmann, 2011, WoLLIC.

Classifying Predicates and Languages

Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann, 1997, Int. J. Found. Comput. Sci..

Zur algorithmischen Synthese von schnellen Programmen

Thomas Zeugmann, 1983 .

Set-driven and rearrangement-independent learning of recursive languages

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1994, Mathematical systems theory.

An Average-Case Optimal One-Variable Pattern Language Learner

Rüdiger Reischuk, Thomas Zeugmann, 2000, J. Comput. Syst. Sci..

Set-driven and rearrangement-independent learning of recursive languages

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1994, Mathematical systems theory.

Editor's Introduction

Rolf Wiehagen, Thomas Zeugmann, Michael M. Richter, 1998, ALT.

Report on COLT 1994

Thomas Zeugmann, 1994, SGAR.

Editors' Introduction

Frank Stephan, Thomas Zeugmann, Vladimir Vovk, 2010, ALT.

Learning One-Variable Pattern Languages Very Efficiently

Thomas Erlebach, Thomas Zeugmann, Hans Stadtherr, 1997 .

Applications of the Differential Calculus

Thomas Zeugmann, Werner Römisch, 2016 .

Guest Editors' foreword

Frank Stephan, Thomas Zeugmann, Vladimir Vovk, 2013, Theor. Comput. Sci..

Inductive Inference of Optimal Programs: A Survey and Open Problems

Thomas Zeugmann, 1990, Nonmonotonic and Inductive Logic.

Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries

Thomas Erlebach, Thomas Zeugmann, Hans Stadtherr, 2001, Theor. Comput. Sci..

Algorithmic learning theory : 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, proceedings

Frank Stephan, Thomas Zeugmann, Sanjay Jain, 2013 .

Learning Recursive Functions Refutably

Rolf Wiehagen, Sanjay Jain, Thomas Zeugmann, 2001, ALT.

Clustering based on Graph Cuts (テーマ:特集「ウェブデータの知的処理」および一般)

Thomas Zeugmann, Jan Poland, 2006 .

Samuel's Checkers Player

Risto Miikkulainen, Scott Sanner, Kristian Kersting, 2010, Encyclopedia of Machine Learning.

Semantic Mapping

Risto Miikkulainen, Scott Sanner, Kristian Kersting, 2010, Encyclopedia of Machine Learning.

A-posteriori Characterizations in Inductive Inference of Recursive Functions

Thomas Zeugmann, T. Zeugmann, 1983, J. Inf. Process. Cybern..

Algorithmic Learning Theory

Peter Auer, Thomas Zeugmann, Sandra Zilles, 2014, Lecture Notes in Computer Science.

Recent Experiences in Parameter-Free Data Mining

Thomas Zeugmann, Yu Zhu, Kimihito Ito, 2010, ISCIS.

Average Case Analysis of Pattern Language Learning Algorithms (Abstract)

Thomas Zeugmann, T. Zeugmann, 1994, AII/ALT.

Epsilon Nets

Thomas Zeugmann, 2010, Encyclopedia of Machine Learning.

Predictive software models

Stan Matwin, Eyke Hüllermeier, Johannes Fürnkranz, 2004, Conference on Semantics in Text Processing.

Learning via queries with teams and anomilies

Thomas Zeugmann, Mark G. Pleszkoch, Efim Kinber, 1990, COLT '90.

On the Power of Recursive Optimizers

Thomas Zeugmann, 1988, Theor. Comput. Sci..

On the Teachability of Randomized Learners

Thomas Zeugmann, Frank J. Balbach, T. Zeugmann, 2006, Complexity of Boolean Functions.

英語で学ぶ計算理論 = Theory of computation

Thomas Zeugmann, 湊真一, 好章大久保, 2009 .

Learning and Consistency

Rolf Wiehagen, Thomas Zeugmann, Rolf Wiehagen, 1995, GOSLER Final Report.

Teaching Learners with Restricted Mind Changes

Thomas Zeugmann, Frank J. Balbach, 2005, ALT.

Characterizations of Monotonic and Dual Monotonic Language Learning

Thomas Zeugmann, Steffen Lange, Shyam Kapur, 1995, Inf. Comput..

Inductive Inference and Language Learning

Thomas Zeugmann, T. Zeugmann, 2006, TAMC.

Characterization of language learning front informant under various monotonicity constraints

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1994, J. Exp. Theor. Artif. Intell..

Algorithmic Learning Theory: 6th International Workshop, ALT '95, Fukuoka, Japan, October 18-20, 1995 - Proceedings

Thomas Zeugmann, Takeshi Shinohara, Klaus P. Jantke, 1995 .

A Unifying Approach to Monotonic Language Learning on Informant

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1992, AII.

Clustering the Normalized Compression Distance for Influenza Virus Data

Thomas Zeugmann, Yu Zhu, Kimihito Ito, 2010, Algorithms and Applications.

PAC Learning

Thomas Zeugmann, 2017, Encyclopedia of Machine Learning and Data Mining.

Stochastic Algorithms: Foundations and Applications, 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings

Osamu Watanabe, Thomas Zeugmann, T. Zeugmann, 2009, SAGA.

Incremental Concept Learning for Bounded Data Mining

John Case, Sanjay Jain, Thomas Zeugmann, 1997, Inf. Comput..

Explanation

Subbarao Kambhampati, Krzysztof Krawiec, Moshe Sipper, 1953, Cladistics : the international journal of the Willi Hennig Society.

Evolutionary Computing

Subbarao Kambhampati, Krzysztof Krawiec, Moshe Sipper, 1997, Lecture Notes in Computer Science.

Guest Editors' foreword

Thomas Zeugmann, Nader H. Bshouty, Gilles Stoltz, 2014, Theor. Comput. Sci..

VC Dimension

Thomas Zeugmann, 2017, Encyclopedia of Machine Learning and Data Mining.

Analyzing the Average-Case Bahavior of Conjunctive Learning Algorithms (Models of Computation and Algorithms)

Rüdiger Reischuk, Thomas Zeugmann, 1999 .

Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data

Thomas Zeugmann, Peter Rossmanith, T. Zeugmann, 1998, ICGI.

Teaching Randomized Learners

Thomas Zeugmann, Frank J. Balbach, 2006, COLT.

On the Parameterised Complexity of Learning Patterns

Ryo Yoshinaka, Frank Stephan, Thomas Zeugmann, 2011, ISCIS.

Classifying Recursive Predicates and Languages

Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann, 1995, GOSLER Final Report.

A Complete and Tight Average-Case Analysis of Learning Monomials

Rüdiger Reischuk, Thomas Zeugmann, R. Reischuk, 1999, STACS.

On the Power of Monotonic Language Learning

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1992 .

On learning of functions refutably

Rolf Wiehagen, Sanjay Jain, Thomas Zeugmann, 2003, Theor. Comput. Sci..

On Barzdin's Conjecture

Thomas Zeugmann, T. Zeugmann, 1986, AII.

The Kahr-Moore-Wang Class Contains Untestable Properties

Thomas Zeugmann, Charles Jordan, T. Zeugmann, 2016, Balt. J. Mod. Comput..

Can Learning in the Limit Be Done Efficiently?

Thomas Zeugmann, T. Zeugmann, 2003, Discovery Science.

Learning languages and functions by erasing

Rolf Wiehagen, Sanjay Jain, Thomas Zeugmann, 2000, Theor. Comput. Sci..

On the Nonboundability of total Effective Operators

Thomas Zeugmann, T. Zeugmann, 1984, Math. Log. Q..

A-022 Relational Properties Expressible with One Universal Quantifier are Testable (Extended Abstract)

Thomas Zeugmann, Charles Jordan, 2009 .

Classification of predicates and languages

Rolf Wiehagen, Thomas Zeugmann, Carl Smith, 1994 .

Epsilon Cover

Thomas Zeugmann, 2017, Encyclopedia of Machine Learning and Data Mining.

Trading monotonicity demands versus mind changes

Thomas Zeugmann, Steffen Lange, 1995, EuroCOLT.

Modeling Incremental Learning from Positive Data

Thomas Zeugmann, Steffen Lange, 1995 .

Language learning in dependence on the space of hypotheses

Thomas Zeugmann, Steffen Lange, 1993, COLT '93.

On the Uniform Learnability of Approximations to Non-Recursive Functions

Frank Stephan, Thomas Zeugmann, T. Zeugmann, 1999, ALT.

On the Influence of Technology on Learning Processes

Rusins Freivalds, Thomas Zeugmann, Ojars Krasts, 2014, Parallel Process. Lett..

One-Sided Error Probabilistic Inductive Inference and Reliable Frequency Identification

Thomas Zeugmann, Efim B. Kinber, T. Zeugmann, 1991, Inf. Comput..

Spike-Timing-Dependent Plasticity

Risto Miikkulainen, Scott Sanner, Kristian Kersting, 2010, Encyclopedia of Machine Learning.

From learning in the limit to stochastic finite learning

Thomas Zeugmann, T. Zeugmann, 2006, Theor. Comput. Sci..

On the Synthesis of Fastest Programs in Inductive Inference

Thomas Zeugmann, 1983, J. Inf. Process. Cybern..

Computing Large Polynomial Powers Very Fast in Parallel

Thomas Zeugmann, 1990, MFCS.

Efficient learning of One-Variable Patttern Languages From Positive Data

Thomas Erlebach, Thomas Zeugmann, Hans Stadtherr, 1996 .

Language Learning with a Bounded Number of Mind Changes

Thomas Zeugmann, Steffen Lange, 1993, STACS.

A-015 Implementation and Evaluation of a Significant Fourier Transform Algorithm

Thomas Zeugmann, 坪和樹, 2010 .

On the Interplay Between Inductive Inference of Recursive Functions, Complexity Theory and Recursive Numberings

Thomas Zeugmann, 2020, CiE.

On the Amount of Nonconstructivity in Learning Recursive Functions

Rusins Freivalds, Thomas Zeugmann, T. Zeugmann, 2011, TAMC.

Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings

Naoki Abe, Thomas Zeugmann, Roni Khardon, 2001, Lecture Notes in Computer Science.

Obituary Rūsiņš Mārtiņš Freivalds (1942-2016)

Thomas Zeugmann, 2016, Bull. EATCS.

Consistent and coherent learning with delta-delay

Thomas Zeugmann, Yohji Akama, 2008, Inf. Comput..

Ignoring data may be the only way to learn efficiently

Rolf Wiehagen, Thomas Zeugmann, Rolf Wiehagen, 1994, J. Exp. Theor. Artif. Intell..

Mathematical Analysis and the Mathematics of Computation

Thomas Zeugmann, Werner Römisch, T. Zeugmann, 2016, Springer International Publishing.

Epsilon Nets

Thomas Zeugmann, 2017, Encyclopedia of Machine Learning and Data Mining.

Consistency Conditions for Inductive Inference of Recursive Functions

Thomas Zeugmann, Yohji Akama, 2006, JSAI.

Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries

Thomas Erlebach, Thomas Zeugmann, Hans Stadtherr, 1997, Theor. Comput. Sci..

Learning a subclass of regular patterns in polynomial time

John Case, Sanjay Jain, Rüdiger Reischuk, 2006, Theor. Comput. Sci..

Co-Learning of Recursive Languages from Positive Data

Rusins Freivalds, Thomas Zeugmann, 1996, Ershov Memorial Conference.

Active Learning of Recursive Functions by Ultrametric Algorithms

Rusins Freivalds, Thomas Zeugmann, T. Zeugmann, 2014, SOFSEM.

Editors' Introduction

Peter Auer, Thomas Zeugmann, Sandra Zilles, 2014, ALT.

Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings

Thomas Zeugmann, Sandra Zilles, Gábor Lugosi, 2009, ALT.

Inductive inference of approximations for recursive concepts

Thomas Zeugmann, Steffen Lange, Gunter Grieser, 2005, Theor. Comput. Sci..

Editors' Introduction

Thomas Zeugmann, Nader H. Bshouty, Gilles Stoltz, 2012, ALT.

Learning Recursive Languages with Bounded Mind Changes

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1993, Int. J. Found. Comput. Sci..

On the Size Complexity of Deterministic Frequency Automata

Rusins Freivalds, Thomas Zeugmann, Grant R. Pogosyan, 2013, LATA.

Indistinguishability and First-Order Logic

Thomas Zeugmann, Charles Jordan, T. Zeugmann, 2008, TAMC.

Guest editors' foreword

Peter Auer, Thomas Zeugmann, Alexander Clark, 2016, Theor. Comput. Sci..

On the Amount of Nonconstructivity in Learning Formal Languages from Positive Data

Sanjay Jain, Frank Stephan, Thomas Zeugmann, 2012, TAMC.

Testable and untestable classes of first-order formulae

Thomas Zeugmann, Charles Jordan, T. Zeugmann, 2012, J. Comput. Syst. Sci..

N-Gram Analysis Based on Zero-Suppressed BDDs

Thomas Zeugmann, Shin-ichi Minato, Ryutaro Kurai, 2006, JSAI.

Too Much Can be Too Much for Learning Efficiently

Rolf Wiehagen, Thomas Zeugmann, 1992, AII.

Guest Editors' introduction

Thomas Zeugmann, Csaba Szepesvári, Jyrki Kivinen, 2014, Theor. Comput. Sci..

Editor’s introduction

Thomas Zeugmann, Takeshi Shinohara, Klaus P. Jantke, 1995, ALT.

Learning Indexed Families of Recursive Languages from Positive Data by

Thomas Zeugmann, Sandra Zilles, Steffen Lange, 2007 .

Clustering the Normalized Compression Distance for Virus Data

Thomas Zeugmann, Kimihito Ito, T. Zeugmann, 2010 .

Clustering the Google Distance with Eigenvectors and Semidefinite Programming

Thomas Zeugmann, Jan Poland, T. Zeugmann, 2012 .

Trading Monotonicity Demands versus Eciency

Thomas Zeugmann, Steffen Lange, 1995 .

The Learnability of Recursive Languages in Dependence on the Hypothesis Space

Thomas Zeugmann, 1993 .

Too Much Information Can Be Too Much for Learning Eciently

Rolf Wiehagen, Thomas Zeugmann, Rolf Wiehagen, 1992 .

TRADING MONOTONICITY DEMANDS VERSUS EFFICIENCY

Thomas Zeugmann, Steffen Lange, T. Zeugmann, 1995 .

Teaching Learners that can only Perform Restricted Mind Changes

Thomas Zeugmann, Frank J. Balbach, 2005 .