Algorithmic Learning Theory
2004
- Learning Boolean Functions in AC0 on Attribute and Classification Noiseby: Akinobu Miyata, Jun Tarui, Etsuji Tomita
- Full Information Game with Gains and Lossesby: Chamy Allenberg-Neeman, Benny Neeman
- A Criterion for the Existence of Predictive Complexity for Binary Gamesby: Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin
- Learning Tree Languages from Positive Examples and Membership Queriesby: Jérôme Besombes, Jean-Yves Marion
- Universal Convergence of Semimeasures on Individual Random Sequencesby: Marcus Hutter, Andrej Muchnik
- Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Spaceby: Steffen Lange, Sandra Zilles
- Approximate Inference in Probabilistic Modelsby: Manfred Opper, Ole Winther
- Learning of Ordered Tree Languages with Height-Bounded Variables Using Queriesby: Satoshi Matsumoto, Takayoshi Shoudai
- Learning Content Sequencing in an Educational Environment According to Student Needsby: Ana Iglesias, Paloma Martínez, Ricardo Aler, Fernando Fernández
- Convergence of a Generalized Gradient Selection Approach for the Decomposition Methodby: Niko List
- On Kernels, Margins, and Low-Dimensional Mappingsby: Maria-Florina Balcan, Avrim Blum, Santosh Vempala
- Learning Languages Generated by Elementary Formal Systems and Its Application to SH Languagesby: Yasuhito Mukouchi, Masako Sato
- Boosting Based on Divide and Mergeby: Eiji Takimoto, Syuhei Koya, Akira Maruoka
- Probabilistic Inductive Logic Programmingby: Luc De Raedt, Kristian Kersting
- Hidden Markov Modelling Techniques for Haplotype Analysisby: Mikko Koivisto, Teemu Kivioja, Heikki Mannila, Pasi Rastas, Esko Ukkonen
- On the Data Consumption Benefits of Accepting Increased Uncertaintyby: Eric Martin, Arun Sharma, Frank Stephan
- The Subsumption Lattice and Query Learningby: Marta Arias, Roni Khardon
- Learnability of Relatively Quantified Generalized Formulasby: Andrei A. Bulatov, Hubie Chen, Víctor Dalmau
- On the Complexity of Working Set Selectionby: Hans-Ulrich Simon
- New Revision Algorithmsby: Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán
- Inductive Inference of Term Rewriting Systems from Positive Databy: M. R. K. Krishna Rao
- Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithmby: Mark Herbster
- Maximum Entropy Principle in Non-ordered Settingby: Victor Maslov, Vladimir V'yugin
- Statistical Learning in Digital Wireless Communicationsby: Toshiyuki Tanaka
- On the Convergence Speed of MDL Predictions for Bernoulli Sequencesby: Jan Poland, Marcus Hutter
- Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributionsby: Keisuke Yamazaki, Sumio Watanabe
- Decision Trees: More Theoretical Justification for Practical Algorithmsby: Amos Fiat, Dmitry Pechyony
- Estimation of the Data Region Using Extreme-Value Distributionsby: Kazuho Watanabe, Sumio Watanabe
- A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networksby: Yoshiyuki Kabashima, Shinsuke Uda
- Prediction with Expert Advice by Following the Perturbed Leader for General Weightsby: Marcus Hutter, Jan Poland
- Complexity of Pattern Classes and Lipschitz Propertyby: Amiran Ambroladze, John Shawe-Taylor
- String Pattern Discoveryby: Ayumi Shinohara
- Applications of Regularized Least Squares to Classification Problemsby: Nicolò Cesa-Bianchi
- Learning Languages from Positive Data and Negative Counterexamplesby: Sanjay Jain, Efim B. Kinber
- Learning, Logic, and Probability: A Unified Viewby: Pedro Domingos
- Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Databy: Daniil Ryabko
- Learning r-of-k Functions by Boostingby: Kohei Hatano, Osamu Watanabe
- Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variablesby: Yusuke Suzuki, Takayoshi Shoudai, Satoshi Matsumoto, Tomoyuki Uchida, Tetsuhiro Miyahara
- Identification with Probability One of Stochastic Deterministic Linear Languagesby: Colin de la Higuera, José Oncina
- Learning Continuous Latent Variable Models with Bregman Divergencesby: Shaojun Wang, Dale Schuurmans
- Intrinsic Complexity of Uniform Learningby: Sandra Zilles
- On the Existence and Convergence of Computable Universal Priorsby: Marcus Hutter
- Robust Inference of Relevant Attributesby: Jan Arpe, Rüdiger Reischuk
- Efficiently Learning the Metric with Side-Informationby: Tijl De Bie, Michinari Momma, Nello Cristianini
- A Stochastic Gradient Descent Algorithm for Structural Risk Minimisationby: Joel Ratsaby
- Kernel Trick Embedded Gaussian Mixture Modelby: Jingdong Wang, Jianguo Lee, Changshui Zhang
- On the Complexity of Training a Single Perceptron with Programmable Synaptic Delaysby: Jirí Síma
- Learning of Erasing Primitive Formal Systems from Positive Examplesby: Jin Uemura, Masako Sato
- Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queriesby: Satoshi Matsumoto, Yusuke Suzuki, Takayoshi Shoudai, Tetsuhiro Miyahara, Tomoyuki Uchida
- Well-Calibrated Predictions from Online Compression Modelsby: Vladimir Vovk
- Association Computation for Information Accessby: Akihiko Takano
- On Ordinal VC-Dimension and Some Notions of Complexityby: Eric Martin, Arun Sharma, Frank Stephan
- Transductive Confidence Machine Is Universalby: Ilia Nouretdinov, Vladimir V'yugin, Alexander Gammerman
- On the Learnability of Erasing Pattern Languages in the Query Modelby: Steffen Lange, Sandra Zilles
- Changing the Inference Type - Keeping the Hypothesis Spaceby: Frank Balbach
- Abduction and the Dualization Problemby: Thomas Eiter
- Efficient Data Representations That Preserve Informationby: Naftali Tishby
- Signal Extraction and Knowledge Discovery Based on Statistical Modelingby: Genshiro Kitagawa
- Learning a Subclass of Regular Patterns in Polynomial Timeby: John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann
- Can Learning in the Limit Be Done Efficiently?by: Thomas Zeugmann
- Criterion of Calibration for Transductive Confidence Machine with Limited Feedbackby: Ilia Nouretdinov, Vladimir Vovk
- How to Achieve Minimax Expected Kullback-Leibler Distance from an Unknown Finite Distributionby: Dietrich Braess, Jürgen Forster, Tomas Sauer, Hans-Ulrich Simon
- An Efficient PAC Algorithm for Reconstructing a Mixture of Linesby: Sanjoy Dasgupta, Elan Pavlov, Yoram Singer
- RBF Neural Networks and Descartes' Rule of Signsby: Michael Schmitt
- On the Learnability of Vector Spacesby: Valentina S. Harizanov, Frank Stephan
- Consistency Queries in Information Extractionby: Gunter Grieser, Klaus P. Jantke, Steffen Lange
- Classification with Intersecting Rulesby: Tony Lindgren, Henrik Boström
- In Search of the Horowitz Factor: Interim Report on a Musical Discovery Projectby: Gerhard Widmer
- Classes with Easily Learnable Subclassesby: Sanjay Jain, Wolfram Menzel, Frank Stephan
- Compactness and Learning of Classes of Unions of Erasing Regular Pattern Languagesby: Jin Uemura, Masako Sato
- Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Controlby: Rémi Coulom
- Reflective Inductive Inference of Recursive Functionsby: Gunter Grieser
- Asymptotic Optimality of Transductive Confidence Machineby: Vladimir Vovk
- Maximizing Agreements and CoAgnostic Learningby: Nader H. Bshouty, Lynn Burroughs
- Minimised Residue Hypotheses in Relevant Logicby: Bertram Fronhöfer, Akihiro Yamamoto
- Learning, Logic, and Topology in a Common Frameworkby: Eric Martin, Arun Sharma, Frank Stephan
- Learning Structure from Sequences, with Applications in a Digital Libraryby: Ian H. Witten
- On Learning Embedded Midbit Functionsby: Rocco A. Servedio
- Data Mining with Graphical Modelsby: Rudolf Kruse, Christian Borgelt
- On Learning Monotone Boolean Functions under the Uniform Distributionby: Kazuyuki Amano, Akira Maruoka
- A Negative Result on Inductive Inference of Extended Pattern Languagesby: Daniel Reidenbach
- On the Smallest Possible Dimension and the Largest Possible Margin of Linear Arrangements Representing Given Concept Classes Uniform Distributionby: Jürgen Forster, Hans-Ulrich Simon
- A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learningby: Johannes Fürnkranz
- Constraint Classification: A New Approach to Multiclass Classificationby: Sariel Har-Peled, Dan Roth, Dav Zimak
- A General Dimension for Approximately Learning Boolean Functionsby: Johannes Köbler, Wolfgang Lindner
- Optimally-Smooth Adaptive Boosting and Application to Agnostic Learningby: Dmitry Gavinsky
- Editors' Introductionby: Nicolò Cesa-Bianchi, Masayuki Numao, Rüdiger Reischuk
- On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrumby: John Shawe-Taylor, Chris Williams, Nello Cristianini, Jaz S. Kandola
- Ordered Term Tree Languages which Are Polynomial Time Inductively Inferable from Positive Databy: Yusuke Suzuki, Takayoshi Shoudai, Tomoyuki Uchida, Tetsuhiro Miyahara
- Large Margin Classification for Moving Targetsby: Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
- Mathematics Based on Learningby: Susumu Hayashi
- On the Absence of Predictive Complexity for Some Gamesby: Yuri Kalnishkan, Michael V. Vyugin
- The Complexity of Learning Concept Classes with Polynomial General Dimensionby: Johannes Köbler, Wolfgang Lindner
- Inference of omega-Languages from Prefixesby: Colin de la Higuera, Jean-Christophe Janodet
- On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classesby: Sandra Zilles
- Learning Intermediate Conceptsby: Stephen Kwek
- Queries Revisitedby: Dana Angluin
- A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithmby: Kohei Hatano
- Learning by Switching Type of Informationby: Sanjay Jain, Frank Stephan
- Refuting Learning Revisitedby: Wolfgang Merkle, Frank Stephan
- Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hardby: Jirí Síma
- Learning Languages in a Unionby: Sanjay Jain, Yen Kaow Ng, Tiong Seng Tay
- Learning How to Separateby: Sanjay Jain, Frank Stephan
- On Learning Correlated Boolean Functions Using Statistical Queriesby: Ke Yang
- Inventing Discovery Tools: Combining Information Visualization with Data Miningby: Ben Shneiderman
- Learning Regular Languages Using RFSAby: François Denis, Aurélien Lemay, Alain Terlutte
- Extending Elementary Formal Systemsby: Steffen Lange, Gunter Grieser, Klaus P. Jantke
- Robot Baby 2001by: Paul R. Cohen, Tim Oates, Niall M. Adams, Carole R. Beal
- Refutable Language Learning with a Neighbor Systemby: Yasuhito Mukouchi, Masako Sato
- Real-Valued Multiple-Instance Learning with Queriesby: Daniel R. Dooly, Sally A. Goldman, Stephen Kwek
- Learning of Boolean Functions Using Support Vector Machinesby: Ken Sadohara
- Learning Coherent Conceptsby: Ashutosh Garg, Dan Roth
- Discovering Mechanisms: A Computational Philosophy of Science Perspectiveby: Lindley Darden
- Learning Recursive Functions Refutablyby: Sanjay Jain, Efim B. Kinber, Rolf Wiehagen, Thomas Zeugmann
- Non-linear Inequalities between Predictive and Kolmogorov Complexitiesby: Michael V. Vyugin, Vladimir V. V'yugin
- A Random Sampling Technique for Training Support Vector Machinesby: José L. Balcázar, Yang Dai, Osamu Watanabe
- Loss Functions, Complexities, and the Legendre Transformationby: Yuri Kalnishkan, Michael V. Vyugin, Volodya Vovk
- Editors' Introductionby: Naoki Abe, Roni Khardon, Thomas Zeugmann
- Efficient Learning of Semi-structured Data from Queriesby: Hiroki Arimura, Hiroshi Sakamoto, Setsuo Arikawa
- The Discovery Science Project in Japanby: Setsuo Arikawa
- Learning Erasing Pattern Languages with Queriesby: Jochen Nessel, Steffen Lange
- The Last-Step Minimax Algorithmby: Eiji Takimoto, Manfred K. Warmuth
- Hypotheses Finding via Residue Hypotheses with the Resolution Principleby: Akihiro Yamamoto, Bertram Fronhöfer
- Learning Recursive Concepts with Anomaliesby: Gunter Grieser, Steffen Lange, Thomas Zeugmann
- A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph Systemby: Tomoyuki Uchida, Yuko Itokawa, Takayoshi Shoudai, Tetsuhiro Miyahara, Yasuaki Nakamura
- Minimum Message Length Grouping of Ordered Databy: Leigh J. Fitzgibbon, Lloyd Allison, David L. Dowe
- Conceptual Classifications Guided by a Concept Hierarchyby: Yuhsuke Itoh, Makoto Haraguchi
- On the Hardness of Learning Acyclic Conjunctive Queriesby: Kouichi Hirata
- Extracting Information from the Web for Concept Learning and Collaborative Filteringby: William W. Cohen
- Towards an Algorithmic Statisticsby: Péter Gács, John Tromp, Paul M. B. Vitányi
- Sequential Sampling Techniques for Algorithmic Learning Theoryby: Osamu Watanabe
- Learning Taxonomic Relation by Case-Based Reasoningby: Ken Satoh
- Identification of Function Distinguishable Languagesby: Henning Fernau
- Dynamic Hand Gesture Recognition Based on Randomized Self-Organizing Map Algorithmby: Tarek El. Tobely, Yuichiro Yoshiki, Ryuichi Tsuda, Naoyuki Tsuruta, Makoto Amamiya
- The Divide-and-Conquer Manifestoby: Thomas G. Dietterich
- On the Noise Model of Support Vector Machines Regressionby: Massimiliano Pontil, Sayan Mukherjee, Federico Girosi
- On Approximate Learning by Multi-layered Feedforward Circuitsby: Bhaskar DasGupta, Barbara Hammer
- Rough Sets and Ordinal Classificationby: Jan C. Bioch, Viara Popova
- Computationally Efficient Transductive Machinesby: Craig Saunders, Alexander Gammerman, Volodya Vovk
- Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Treesby: Tobias Scheffer
- Self-Duality of Bounded Monotone Boolean Functions and Related Problemsby: Daya Ram Gaur, Ramesh Krishnamurti
- A Probabilistic Identification Resultby: Eric McCreath
- A Note on the Generalization Performance of Kernel Classifiers with Marginby: Theodoros Evgeniou, Massimiliano Pontil
- Learning from Positive and Unlabeled Examplesby: Fabien Letouzey, François Denis, Rémi Gilleron
- Sharper Bounds for the Hardness of Prototype and Feature Selectionby: Richard Nock, Marc Sebban
- Generalization Error of Limear Neural Networks in Unidentifiable Casesby: Kenji Fukumizu
- Predicting Nearly as well as the best Pruning of a Planar Decision Graphby: Eiji Takimoto, Manfred K. Warmuth
- Learning Real Polynomials with a Turing Machineby: Dennis Cheung
- Positive and Unlabeled Examples Help Learningby: Francesco De Comité, François Denis, Rémi Gilleron, Fabien Letouzey
- Extended Stochastic Complexity and Minimax Relative Loss Analysisby: Kenji Yamanishi
- Learning from Random Textby: Peter Rossmanith
- Complexity in the Case against Accuracy: When Building one Function-Free Horn Clause is as Hard as Anyby: Richard Nock
- Inductive Learning with Corroborationby: Phil Watson
- Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithmby: Carlos Domingo
- Flattening and Implicationby: Kouichi Hirata
- Theoretical Views of Boosting and Applicationsby: Robert E. Schapire
- Finding Relevant Variables in PAC Model with Membership Queriesby: David Guijarro, Jun Tarui, Tatsuie Tsukiji
- Learnability of Enumerable Classes of Recursive Functions from "Typical" Examplesby: Jochen Nessel
- On the Uniform Learnability of Approximations to Non-Recursive Functionsby: Frank Stephan, Thomas Zeugmann
- Algebraic Analysis for Singular Statistical Estimationby: Sumio Watanabe
- On the Strength of Incremental Learningby: Steffen Lange, Gunter Grieser
- PAC Learning with Nasty Noiseby: Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz
- The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract)by: José L. Balcázar, Jorge Castro, David Guijarro, Hans-Ulrich Simon
- A Note on Support Vector Machine Degeneracyby: Ryan M. Rifkin, Massimiliano Pontil, Alessandro Verri
- Boolean Formulas are Hard to Learn for most Gate Basesby: Víctor Dalmau
- Learning Minimal Covers of Functional Dependencies with Queriesby: Montserrat Hermo, Víctor Lavín
- The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasaby: Jirí Wiedermann
- On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spacesby: Theodoros Evgeniou, Massimiliano Pontil
- Tailoring Representations to Different Requirementsby: Katharina Morik
- Induction of Logic Programs Based on psi-Termsby: Yutaka Sasaki
- A Method of Similarity-Driven Knowledge Revision for Type Specializationsby: Nobuhiro Morita, Makoto Haraguchi, Yoshiaki Okubo
- On Learning Unions of Pattern Languages and Tree Patternsby: Sally A. Goldman, Stephen Kwek
- The VC-Dimension of Subclasses of Patternby: Andrew R. Mitchell, Tobias Scheffer, Arun Sharma, Frank Stephan
- Genral Linear Relations among Different Types of Predictive Complexityby: Yuri Kalnishkan
- Lower Bounds for the Complexity of Learning Half-Spaces with Membership Queriesby: Valery N. Shevchenko, Nikolai Yu. Zolotykh
- Closedness Properties in EX-Identification of Recursive Functionsby: Kalvis Apsitis, Rusins Freivalds, Raimonds Simanovskis, Juris Smotrovs
- On the Sample Complexity for Neural Treesby: Michael Schmitt
- Towards the Validation of Inductive Learning Systemsby: Gunter Grieser, Klaus P. Jantke, Steffen Lange
- Learning with Refutationby: Sanjay Jain
- Learning to Win Process-Control Games Watching Game-Mastersby: John Case, Matthias Ott, Arun Sharma, Frank Stephan
- Learnability of Translations from Positive Examplesby: Noriko Sugimoto
- Synthesizing Learners Tolerating Computable Noisy Databy: John Case, Sanjay Jain
- Editor's Introductionby: Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann
- Learning from Entailment of Logic Programs with Local Variablesby: M. R. K. Krishna Rao, Abdul Sattar
- Cryptographic Limitations on Parallelizing Membership and Equivalence Queries with Applications to Random Self-Reductionsby: Marc Fischlin
- Learning Unary Output Two-Tape Automata from Multiplicity and Equivalence Queriesby: Giovanna Melideo, Stefano Varricchio
- Learning Sub-classes of Monotone DNF on the Uniform Distributionby: Karsten A. Verbeurgt
- Learning Algebraic Structures from Text Using Semantical Knowledgeby: Frank Stephan, Yuri Ventsov
- PAC Learning from Positive Statistical Queriesby: François Denis
- A Comparison of Identification Criteria for Inductive Inference of Recursive Real-Valued Functionsby: Eiju Hirowatari, Setsuo Arikawa
- Computational Aspects of Parallel Attribute-Efficient Learningby: Peter Damaschke
- Locality, Reversibility, and Beyond: Learning Languages from Positive Databy: Tom Head, Satoshi Kobayashi, Takashi Yokomori
- Using Attribute Grammars for Description of Inductive Inference Search Spaceby: Ugis Sarkans, Janis Barzdins
- Analysis of Case-Based Representability of Boolean Functions by Monotone Theoryby: Ken Satoh
- Structured Weight-Based Prediction Algorithmsby: Akira Maruoka, Eiji Takimoto
- Logical Aspects of Several Bottom-Up Fittingsby: Akihiro Yamamoto
- Scalability Issues in Inductive Logic Programmingby: Stefan Wrobel
- Comparing the Power of Probabilistic Learning and Oracle Identification Under Monotonicity Constraintsby: Léa Meyer
- LIME: A System for Learning Relationsby: Eric McCreath, Arun Sharma
- Consistent Polynominal Identification in the Limitby: Werner Stein
- Predictive Learning Models for Concept Driftby: John Case, Sanjay Jain, Susanne Kaufmann, Arun Sharma, Frank Stephan
- Characteristic Sets for Unions of Regular Pattern Languages and Compactnessby: Masako Sato, Yasuhito Mukouchi, Dao Zheng
- A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databasesby: Hiroki Arimura, Atsushi Wataki, Ryoichi Fujino, Setsuo Arikawa
- Finding a One-Variable Pattern from Incomplete Databy: Hiroshi Sakamoto
- Program Error Detection/Correction: Turning PAC Learning into PERFECT Learning (Abstract)by: Manuel Blum
- Partial Occam's Razor and Its Applicationsby: Carlos Domingo, Tatsuie Tsukiji, Osamu Watanabe
- Team Learning as a Gameby: Andris Ambainis, Kalvis Apsitis, Rusins Freivalds, William I. Gasarch, Carl H. Smith
- Learning Disjunctions of Featuresby: Stephen Kwek
- Deranomized Learning of Boolean Functionsby: Meera Sitharam, Timothy Straney
- Synthesizing Noise-Tolerant Language Learnersby: John Case, Sanjay Jain, Arun Sharma
- On Exploiting Knowledge and Concept Use in Learning Theoryby: Leonard Pitt
- Learning One-Variable Pattern Languages Very Efficiently on Average, in Parallel, and by Asking Queriesby: Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, Thomas Zeugmann
- Classical Brouwer-Heyting-Kolmogorov Interpretationby: Masahiko Sato
- Learning of R.E. Languages from Good Examplesby: Sanjay Jain, Steffen Lange, Jochen Nessel
- Probability Theory for the Brier Gameby: V. G. Vovk
- PAC Learning under Helpful Distributionsby: François Denis, Rémi Gilleron
- A Simple Algorithm for Predicting Nearly as Well as the Best Pruning Labeled with the Best Prediction Values of a Decision Treeby: Eiji Takimoto, Ken'ichi Hirai, Akira Maruoka
- Inferability of Recursive Real-Valued Functionsby: Eiju Hirowatari, Setsuo Arikawa
- On Learning Disjunctions of Zero-One Treshold Functions with Queriesby: Tibor Hegedüs, Piotr Indyk
- Learning DFA from Simple Examplesby: Rajesh Parekh, Vasant Honavar
- PAC Learning Using Nadaraya-Watson Estimator Based on Orthonormal Systemsby: Hongzhu Qiao, Nageswara S. V. Rao, Vladimir Protopopescu
- A Note on a Scale-Sensitive Dimension of Linear Bounded Functionals in Banach Spacesby: Leonid Gurvits
- Identifiability of Subspaces and Homomorphic Images of Zero-Reversible Languagesby: Satoshi Kobayashi, Takashi Yokomori
- On the Relevance of Time in Neural Computation and Learningby: Wolfgang Maass
- An Efficient Exact Learning Algorithm for Ordered Binary Decision Diagramsby: Atsuyoshi Nakamura
- Oracles in Sigmap2 are Sufficient for Exact Learningby: Johannes Köbler, Wolfgang Lindner
- Exact Learning via Teaching Assistants (Extended Abstract)by: Vikraman Arvind, N. V. Vinodchandran
- Learning and Revising Theories in Noisy Domainsby: Xiaolong Zhang, Masayuki Numao
- Effects of Kolmogorov Complexity Present in Inductive Inference as Wellby: Andris Ambainis, Kalvis Apsitis, Cristian Calude, Rusins Freivalds, Marek Karpinski, Tomas Larfeldt, Iveta Sala, Juris Smotrovs
- Polynomial Time Inductive Inference of Regular Term Tree Languages from Positive Databy: Satoshi Matsumoto, Yukiko Hayashi, Takayoshi Shoudai
- Learning Simple Deterministic Finite-Memory Automataby: Hiroshi Sakamoto
- Inferring a System from Examples with Time Passageby: Yasuhito Mukouchi
- Learning Acyclic First-Order Horn Sentences from Entailmentby: Hiroki Arimura
- Monotone Extensions of Boolean Data Setsby: Endre Boros, Toshihide Ibaraki, Kazuhisa Makino
- Learning a Representation for Optimizable Formulasby: Hans Kleine Büning, Theodor Lettmann
- Reducing Complexity of Decision Trees with Two Variable Testsby: R. A. Pearson, E. K. T. Smith
- Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithmby: Nada Lavrac, Dragan Gamberger, Peter D. Turney
- Inductive Logic Programming Beyond Logical Implicationby: Jianguo Lu, Jun Arima
- A Class of Prolog Programs Inferable from Positive Databy: M. R. K. Krishna Rao
- Managing Complexity in Neurodial Circuitsby: Leslie G. Valiant
- Improved Bounds about On-line Learning of Smooth Functions of a Single Variableby: Philip M. Long
- Vacillatory and BC Learning on Noisy Databy: John Case, Sanjay Jain, Frank Stephan
- On Learning and Co-learning of Minimal Programsby: Sanjay Jain, Efim B. Kinber, Rolf Wiehagen
- Reflecting Inductive Inference Machines and Its Improvement by Therapyby: Gunter Grieser
- Learnability of Exclusive-Or Expansion Based on Monotone DNF Formulasby: Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka
- Effects of Feature Selection with 'Blurring' on NeuroFuzzy Systemsby: Selwyn Piramuthu
- The Kindest Cut: Minimum Message Length Segmentationby: Rohan A. Baxter, Jonathan J. Oliver
- Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosoisby: Dragan Gamberger, Nada Lavrac, Saso Dzeroski
- Learning by Erasingby: Steffen Lange, Rolf Wiehagen, Thomas Zeugmann
- Query Learning of Bounded-Width OBDDsby: Atsuyoshi Nakamura
- Transformations that Preserve Learnabilityby: Andris Ambainis, Rusins Freivalds
- Inductive Inference of Unbounded Unions of Pattern Languages from Positive Databy: Takeshi Shinohara, Hiroki Arimura
- Limits of Exact Algorithms For Inference of Minimum Size Finite State Machinesby: Arlindo L. Oliveira, Stephen Edwards
- Probabilitic Limit Identification up to "Small" Setsby: Juris Viksna
- The Complexity of Exactly Learning Algebraic Concepts. (Extended Abstract)by: Vikraman Arvind, N. V. Vinodchandran
- Incorporating Hypothetical Knowledge into the Process of Inductive Synthesisby: Janis Barzdins, Ugis Sarkans
- Constructive Learning of Translations Based on Dictionariesby: Noriko Sugimoto, Kouichi Hirata, Hiroki Ishizaka
- Efficient Learning of Real Time Two-Counter Automata (Extended Abstract)by: Amr F. Fahmy, Robert S. Roos
- MML Estimation of the Parameters of the Sherical Fisher Distributionby: David L. Dowe, Jonathan J. Oliver, Chris S. Wallace
- Induction of Constraint Logic Programsby: Lionel Martin, Christel Vrain
- Boosting First-Order Learningby: J. Ross Quinlan
- Genetic Fitness Optimization Using Rapidly Mixing Markov Chainsby: Paul M. B. Vitányi
- Learning Nested Differences in the Presence of Malicious Noiseby: Peter Auer
- Learning Sparse Linear Combinations of Basis Functions over a Finite Domainby: Atsuyoshi Nakamura, Shinji Miura
- Inductive Constraint Logicby: Luc De Raedt, Wim Van Laer
- Incremental Learning of Logic Programsby: M. R. K. Krishna Rao
- Editor's Introductionby: Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann
- Complexity of Network Training for Classes of Neural Networksby: Charles C. Pinter
- Machine Induction Without Revolutionary Paradigm Shiftsby: John Case, Sanjay Jain, Arun Sharma
- Learning Strongly Deterministic Even Linear Languages from Positive Examplesby: Takeshi Koshiba, Erkki Mäkinen, Yuji Takada
- Reflecting and Self-Confident Inductive Inference Machinesby: Klaus P. Jantke
- Grammatical Inference: An Old and New Paradigmby: Yasubumi Sakakibara
- Simple PAC Learning of Simple Decision Listsby: Jorge Castro, José L. Balcázar
- The Complexity of Learning Minor Closed Graph Classesby: Carlos Domingo, John Shawe-Taylor
- Learning Ordered Binary Decision Diagramsby: Ricard Gavaldà, David Guijarro
- Simulating Teams with Many Conjecturesby: Bala Kalyanasundaram, Mahendran Velauthapillai
- On Approximately Identifying Concept Classes in the Limitby: Satoshi Kobayashi, Takashi Yokomori
- Learning Orthogonal F-Horn Formulasby: Akira Miyashiro, Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka
- Inferring a DNA Sequence from Erroneous Copies (Abstract)by: John D. Kececioglu, Ming Li, John Tromp
- Probabilistic Language Learning Under Monotonicity Constraintsby: Léa Meyer
- Application of Kolmogorov Complexity to Inductive Inference with Limited Memoryby: Andris Ambainis
- Technical and Scientific Issues of KDD (or: Is KDD a Science?)by: Yves Kodratoff
- Efficient Learning of Real Time One-Counter Automataby: Amr F. Fahmy, Robert S. Roos
- Learning Unions of Tree Patterns Using Queriesby: Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara
- Noisy Inference and Oraclesby: Frank Stephan
- Language Learning from Membership Queries and Characteristic Examplesby: Hiroshi Sakamoto
- Analogical Logic Program Synthesis Algorithm That Can Refute Inappropriate Similaritiesby: Ken Sadohara, Makoto Haraguchi
- Classification Using Informationby: William I. Gasarch, Mark G. Pleszkoch, Mahendran Velauthapillai
- A Note on Learning DNF Formulas Using Equivalence and Incomplete Membership Queriesby: Zhixiang Chen
- Monotonicity versus Efficiency for Learning Languages from Textsby: Efim B. Kinber
- Fuzzy Analogy Based Reasoning and Classification of Fuzzy Analogiesby: Toshiharu Iwatani, Shun'ichi Tano, Atsushi Inoue, Wataru Okamoto
- Towards Realistic Theories of Learningby: Naoki Abe
- A Calculus for Logical Clusteringby: Shuo Bai
- Program Synthesis in the Presence of Infinite Number of Inaccuraciesby: Sanjay Jain
- Training Diagraphsby: Hsieh-Chang Tu, Carl H. Smith
- On-line Learning with Malicious Noise and the Closure Algorithmby: Peter Auer, Nicolò Cesa-Bianchi
- Derived Sets and Inductive Inferenceby: Kalvis Apsitis
- On Monotonic Strategies for Learning r.e. Languagesby: Sanjay Jain, Arun Sharma
- Inductive Inference of Monogenic Pure Context-free Languagesby: Noriyuki Tanida, Takashi Yokomori
- Identifying Regular Languages over Partially-Commutative Monoidsby: Claudio Ferretti, Giancarlo Mauri
- A Unified Approach to Inductive Logic and Case-Based Reasoning (Extended Abstract)by: Michael M. Richter
- Mutual Information Gaining Algorithm and Its Relation to PAC-Learning Algorithmby: Eiji Takimoto, Ichiro Tajika, Akira Maruoka
- Rule-Generating Abduction for Recursive Prologby: Kouichi Hirata
- Language Learning under Various Types of Constraint Combinationsby: Shyam Kapur
- Learnability with Restricted Focus of Attention guarantees Noise-Toleranceby: Shai Ben-David, Eli Dichterman
- Co-learnability and FIN-identifiability of Enumerable Classes of Total Recursive Functionsby: Rusins Freivalds, Dace Gobleja, Marek Karpinski, Carl H. Smith
- On Case-Based Representability and Learnability of Languagesby: Christoph Globig, Steffen Lange
- Towards Efficient Inductive Synthesis from Input/Output Examples (Abstract)by: Janis Barzdins
- Efficient Distribution-free Population Learning of Simple Conceptsby: Atsuyoshi Nakamura, Naoki Abe, Jun-ichi Takeuchi
- Machine Discovery in the Presence of Incomplete or Ambiguous Databy: Steffen Lange, Phil Watson
- Set-Driven and Rearrangement-Independent Learning of Recursive Languagesby: Steffen Lange, Thomas Zeugmann
- Deductive Plan Generationby: Wolfgang Bibel, Michael Thielscher
- Efficient Learning of Regular Expressions from Good Examplesby: Alvis Brazma, Karlis Cerans
- Synthesis Algorithm for Recursive Process by µ-calculus (Extended Abstract)by: Shigetomo Kimura, Atsushi Togashi, Norio Shiratori
- Language Learning from Good Examplesby: Steffen Lange, Jochen Nessel, Rolf Wiehagen
- Learning Concatenations of Locally Testable Languages from Positive Databy: Satoshi Kobayashi, Takashi Yokomori
- Enumerable Classes of Total Recursive Functions: Complexity of Inductive Inferenceby: Andris Ambainis, Juris Smotrovs
- Learning with Higher Order Additional Informationby: Ganesh Baliga, John Case
- From Specifications to Programs: Induction in the Service of Synthesis (Abstract)by: Nachum Dershowitz
- Three Decades of Team Learningby: Carl H. Smith
- Constructive Induction for Recursive Programsby: Chowdhury Rahman Mofizur, Masayuki Numao
- Refutably Probably Approximately Correct Learningby: Satoshi Matsumoto, Ayumi Shinohara
- Finding Tree Patterns Consistent with Positive and Negative Examples Using Queriesby: Hiroki Ishizaka, Hiroki Arimura, Takeshi Shinohara
- Therapy Plan Generation as Program Synthesisby: Oksana Arnold, Klaus P. Jantke
- Identifying Nearly Minimal Gödel Numbers From Additional Informationby: Rusins Freivalds, Ognian Botuscharov, Rolf Wiehagen
- Learning Languages by Collecting Cases and Tuning Parametersby: Yasubumi Sakakibara, Klaus P. Jantke, Steffen Lange
- Constructing Predicate Mappings for Goal-Dependent Abstractionby: Yoshiaki Okubo, Makoto Haraguchi
- Explanation-Based Reuse of Prolog Programsby: Yasuyuki Koga, Eiju Hirowatari, Setsuo Arikawa
- Efficient Algorithm for Learning Simple Regular Expressions from Noisy Examplesby: Alvis Brazma
- Average Case Analysis of Pattern Language Learning Algorithms (Abstract)by: Thomas Zeugmann
- Learning from Examples with Typed Equational Programmingby: Akira Ishino, Akihiro Yamamoto
- Inductive Inference of an Approximate Concept from Positive Databy: Yasuhito Mukouchi
- A Decomposition Based Induction Model for Discovering Concept Clusters from Databasesby: Ning Zhong, Setsuo Ohsuga
- Towards Efficient Inductive Synthesis of Expressions from Input/Output Examplesby: Janis Barzdins, Guntis Barzdins, Kalvis Apsitis, Ugis Sarkans
- On the Sample Complexity of Consistent Learning with One-Sided Errorby: Eiji Takimoto, Akira Maruoka
- A Typed Lambda-Calculus for Proving-by-Example and Bottom-Up Generalization Procedureby: Masami Hagiya
- Exact Learning of Linear Combinations of Monotone Terms from Function Value Queriesby: Atsuyoshi Nakamura, Naoki Abe
- Complexity of Computing Vapnik-Chervonenkis Dimensionby: Ayumi Shinohara
- Thue Systems and DNA - A Learning Algorithm for a Subclassby: Rani Siromoney, D. G. Thomas, K. G. Subramanian, V. Rajkumar Dare
- Properties of Language Classes With Finite Elasticityby: Takashi Moriyama, Masako Sato
- On Aggregating Teams of Learning Machinesby: Sanjay Jain, Arun Sharma
- Optimal Layered Learning: A PAC Approach to Incremental Samplingby: Stephen Muggleton
- Neural Discriminant Analysisby: Jorge Ricardo Cuellar, Hans-Ulrich Simon
- How to Invent Characterizable Inference Methods for Regular Languagesby: Timo Knuutila
- Reformulation of Explanation by Linear Logic: Toward Logic for Explanationby: Jun Arima, Hajime Sawamura
- Uniform Charakterizations of Various Kinds of Language Learningby: Shyam Kapur
- Induction of Probabilistic Rules Based on Rough Set Theoryby: Shusaku Tsumoto, Hiroshi Tanaka
- Learning Theory Toward Genome Informaticsby: Satoru Miyano
- On the VC-Dimension of Depth Four Threshold Circuits and the Complexity of Boolean-Valued Functionsby: Akito Sakurai
- Unifying Learning Methods by Colored Digraphsby: Kenichi Yoshida, Hiroshi Motoda, Nitin Indurkhya
- Identifying and Using Patterns in Sequential Databy: Philip Laird
- On the Duality Between Mechanistic Learners and What it is They Learnby: Rusins Freivalds, Carl H. Smith
- Inductive Resolutionby: Taisuke Sato, Sumitaka Akiba
- Inductive Inference Machines That Can Refute Hypothesis Spacesby: Yasuhito Mukouchi, Setsuo Arikawa
- Generalized Unification as Background Knowledge in Learning Logic Programsby: Akihiro Yamamoto
- Case-Based Representation and Learning of Pattern Languagesby: Klaus P. Jantke, Steffen Lange
- Learning Strategies Using Decision Listsby: Satoshi Kobayashi
- Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilitiesby: Robert P. Daley, Bala Kalyanasundaram
- Learning With Growing Qualityby: Juris Viksna
- Epsilon-Approximations of k-label Spacesby: Susumu Hasegawa, Hiroshi Imai, Masaki Ishiguro
- Algebraic Structure of Some Learning Systemsby: Jean-Gabriel Ganascia
- A New Algorithm for Automatic Configuration of Hidden Markov Modelsby: Makoto Iwayama, Nitin Indurkhya, Hiroshi Motoda
- A Perceptual Criterion for Visually Controlling Learningby: Masaki Suwa, Hiroshi Motoda
- The VC-Dimensions of Finite Automata with n Statesby: Yoshiyasu Ishigami, Sei'ichi Tani
- Regularization Learning of Neural Networks for Generalizationby: Shotaro Akaho
- Polynomial-Time MAT Learning of Multilinear Logic Programsby: Kimihito Ito, Akihiro Yamamoto
- Discovery Learning in Intelligent Tutoring Systemsby: Setsuko Otsuki
- Prudence in Vacillatory Language Identification (Extended Abstract)by: Sanjay Jain, Arun Sharma
- Competitive Learning by Entropy Minimizationby: Ryotaro Kamimura
- Monotonic Language Learningby: Shyam Kapur
- On Learning Systolic Languagesby: Takashi Yokomori
- Protein Secondary Structure Prediction Based on Stochastic-Rule Learningby: Hiroshi Mamitsuka, Kenji Yamanishi
- A Stochastic Approach to Genetic Information Processingby: Akihiko Konagaya
- On PAC Learnability of Functional Dependenciesby: Tatsuya Akutsu, Atsuhiro Takasu
- An Application Of Bernstein Polynomials in PAC Modelby: Masahiro Matsuoka
- A Note on the Query Complexity of Learning DFA (Extended Abstract)by: José L. Balcázar, Josep Díaz, Ricard Gavaldà, Osamu Watanabe
- Notes on the PAC Learning of Geometric Concepts with Additional Informationby: Ken-ichiro Kakihara, Hiroshi Imai
- Efficient Inductive Inference of Primitive Prologs from Positive Databy: Hiroki Ishizaka, Hiroki Arimura, Takeshi Shinohara
- Learning k-Term Monotone Boolean Formulaeby: Yoshifumi Sakai, Akira Maruoka
- From Inductive Inference to Algorithmic Learning Theoryby: Rolf Wiehagen
- Domains of Attraction in Autoassociative Memory Networks for Character Pattern Recognitionby: Koichi Niijima
- Planning with Abstraction Based on Partial Predicate Mappingsby: Yoshiaki Okubo, Makoto Haraguchi
- Implementation of Heuristic Problem Solving Process Including Analogical Reasoningby: Kazuhiro Ueda, Saburo Nagano
- Some Improved Sample Complexity Bounds in the Probabilistic PAC Learning Modelby: Jun-ichi Takeuchi
- Iterative Weighted Least Squares Algorithms for Neural Networks Classifiersby: Takio Kurita
- Inductive Inference with Bounded Mind Changesby: Yasuhito Mukouchi
- Reliable and Useful Learning with Uniform Probability Distributionsby: Jyrki Kivinen
- Synthesis of Rewrite Programs by Higher-Order and Semantic Unificationby: Masami Hagiya
- Mathematical Theory of Neural Learningby: Shun-ichi Amari
- Inductive Inference of Monotonic Formal Systems from Positive Databy: Takeshi Shinohara
- Exact Learning of Semilinear Setsby: Yuji Takada, Kunihiko Hiraishi, Yasubumi Sakakibara
- Learning Commutative Deterministic Finite State Automata in Polynomial Timeby: Naoki Abe
- A Self-Organizing Network Composed of Symbol Nodes With Location Parameterby: Ryuichi Oka
- Analog by Simulation - A Weak Justification Method (Preliminary Report)by: Jun Arima
- Model Inference of Constrained Recursive Figuresby: Shuling Liu, Masami Hagiya
- Anomalous Learning Helps Succinctness (Extended Abstract)by: John Case, Sanjay Jain, Arun Sharma
- On the Role of Interpretive Analogy in Learningby: Bipin Indurkhya
- Inductive Logic Programmingby: Stephen Muggleton
- On Paraphrasing-Based Analogical Reasoningby: Ryohei Orihara, Akihiko Osuga, Yoichi Kusui
- Monotonic and Non-Monotonic Inductive Inferenceby: Klaus P. Jantke
- Learning Equal Matrix Grammers and Multitape Automata with Structural Informationby: Yuji Takada
- Occam Algorithms for Learning From Noisy Examplesby: Yasubumi Sakakibara
- Deciding What to Learn in Explanation-Based Macro-Rule Learningby: Toshikazu Tanaka
- Learning Locally Testable Languages in the Strict Senseby: Pedro Garcia, Enrique Vidal, José Oncina
- EBG and Term-Rewriting Systemsby: Philip Laird, Evan Gamble
- The Algorithms of Recognition of the Functional Sites in Genetic Textsby: Vladimir Gusev, Nadia A. Chuzhanova
- Polynomial-Time Inference of Pattern Languagesby: Steffen Lange, Rolf Wiehagen
- Efficient Induction of Logic Programsby: Stephen Muggleton, Cao Feng
- Learning the Distribution in the Extended PAC Modelby: Nicolò Cesa-Bianchi
- The Function-Acquisition Paradigm in a Knowledge-Based Concept-Synthesis Environmentby: B. Shekar, M. Narasimha Murty, G. Krishna
- Teachability in Computational Learningby: Ayumi Shinohara, Satoru Miyano
- Inductive Inference of Term Rewriting Systems Realizing Algebrasby: Atsushi Togashi, Shoichi Noguchi
- Approximate Inference and Scientific Methodby: Mark A. Fulk, Sanjay Jain
- Structured Neural Networks and Their Flash Learningby: Akira Namatame
- Decision Theoretic Generalizations of the PAC Learning Modelby: David Haussler
- Analogical Reasoning Based on Higher-Order Unificationby: Masateru Harao
- On Inductive Learning for Three Kinds of Data Structuresby: Tatsuo Unemi
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