| 1997 | |  | On-Line Maximum Likelihood Prediction with Respect to General Loss Functions - Kenji Yamanishi |
| | | Sample compression, learnability, and the Vapnik-Chervonenkis dimension - Manfred Warmuth |
| | | A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization - Thorsten Joachims |
| | | On the relevance of time in neural computation and learning - Wolfgang Maass |
| | | Linear algebraic proofs of VC-dimension based inequalities - Leonid Gurvits |
| | | Stochastic Complexity in Learning - Jorma Rissanen |
| | | Clausal Discovery - Luc De Raedt and Luc Dehaspe |
| | | PAC Adaptive Control of Linear Systems - Claude-Nicolas Fiechter |
| | | Bayesian network classifiers - Nir Friedman, Dan Geiger and Moises Goldszmidt |
| | | A Bayesian approach to model learning in non-Markovian environments - N. Suematsu, A. Hayashi and S. Li |
| | | Online learning versus offline learning - Shai Ben-David, Eyal Kushilevitz and Yishay Mansour |
| | | Confidence estimates of classification accuracy on new examples - John Shawe-Taylor |
| | | A brief look at some machine learning problems in genomics - David Haussler |
| | | Declarative bias in equation discovery - Ljupčo Todorovski and Sašo Džeroski |
| | | Malicious omissions and errors in answers to membership queries - Dana Angluin, Mārtiņš Krikis, Robert H. Sloan and György Turán |
| | | Characterizing Language Learning in Terms of Computable Numberings - Sanjay Jain and Arun Sharma |
| | | Probabilistic language learning under monotonicity constraint - Léa Meyer |
| | | Learning Distributions from Random Walks - Funda Ergün, S. Ravi Kumar and Ronitt Rubinfeld |
| | | Learning and revising theories in noisy domains - Xiaolong Zhang and Masayuki Numao |
| | | Probabilistic self-structuring and learning - David Garvin and Peter Rayner |
| | | A comparison of new and old algorithms for a mixture estimation problem - D. Helmbold, R. E. Schapire, Y. Singer and M. K. Warmuth |
| | | Learning nearly monotone k-term DNF - Jorge Castro, David Guijarro and Victor Lavin |
| | | Dynamic modeling of chaotic time series by neural networks - Gustavo Deco and Bernd Schürmann |
| | | Closedness properties in team learning of recursive functions - Juris Smotrovs |
| | | A random sampling based algorithm for learning the intersection of half-spaces (extended abstract) - Santosh Vempala |
| | | Learning From Examples With Unspecified Attribute Values - Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott |
| | | Preface - S. Arikawa and M. M. Richter |
| | | Learning string edit distance - Eric Sven Ristad and Peter N. Yianilos |
| | | Efficient locally weighted polynomial regression predictions - Andrew W. Moore, Jeff Schneider and Kan Deng |
| | | Approximate testing and its relationship to learning - Kathleen Romanik |
| | | On the optimality of the simple Bayesian classifier under zero-one loss - Pedro Domingos and Michael Pazzani |
| | | Identifiability of subspaces and homomorphic images of zero-reversible languages - Satoshi Kobayashi and Takashi Yokomori |
| | | Supervised learning using labeled and unlabeled examples - Geoffrey Towell |
| | | A Bayesian/information theoretic model of learning to learn via multiple task sampling - Jonathan Baxter |
| | | Using output codes to boost multiclass learning problems - Robert E. Schapire |
| | | Learning DFA from simple examples - Rajesh Parekh and Vasant Honavar |
| | | Instance pruning techniques - D. Randall Wilson and Tony R. Martinez |
| | | Learning unions of tree patterns using queries - Hiroki Arimura, Hiroki Ishizaka and Takeshi Shinohara |
| | | ARACHNID: Adaptive retrieval agents choosing heuristic neighborhoods for information discovery - Filippo Menczer |
| | | Classifying Predicates and Languages - Carl H. Smith, Rolf Wiehagen and Thomas Zeugmann |
| | | Learning to Reason - Roni Khardon and Dan Roth |
| | | A comparison of RBF and MLP networks for classification of biomagnetic fields - Martin F. Schlang, Klaus Abraham-Fuchs, Ralph Neuneier and Johann Uebler |
| | | Learning from Multiple Sources of Inaccurate Data - Ganesh Baliga, Sanjay Jain and Arun Sharma |
| | | PAC learning of concept classes through the boundaries of their items - B. Apolloni and S. Chiaravalli |
| | | A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting - Yoav Freund and Robert E. Schapire: |
| | | The sample complexity of learning fixed-structure Bayesian networks - Sanjoy Dasgupta |
| | | Inferring a DNA sequence from erroneous copies - John Kececioglu, Ming Li and John Tromp |
| | | Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries - Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger and Thomas Zeugmann |
| | | A practical approach for evaluating generalization performance - Marjorie Klenin |
| | | Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain - Avrim Blum |
| | | Towards Realistic Theories of Learning - N. Abe |
| | | On learning from multi-instance examples: Empirical evaluation of a theoretical approach - Peter Auer |
| | | Classical Brouwer-Heyting-Kolmogorov interpretation - Masahiko Sato |
| | | Learning counting functions with queries - Zhixiang Chen and Steven Homer |
| | | Multitask learning - Rich Caruana |
| | | A result relating convex n-widths to covering numbers with some applications to neural networks - Jonathan Baxter and Peter Bartlett |
| | | Learning an Intersection of a Constant Number of Halfspaces over a Uniform Distribution - Avrim Blum and Ravindran Kannan |
| | | Selective sampling using the query by committee algorithm - Yoav Freund, H. Sebastian Seung, Eli Shamir and Naftali Tishby |
| | | Learning about the Parameter of the Bernoulli Model - V. G. Vovk |
| | | Characterizing the generalization performance of model selection strategies - Dale Schuurmans, Lyle H. Ungar and Dean P. Foster |
| | | Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement - Jürgen Schmidhuber, Jieyu Zhao and Marco Wiering |
| | | Learning pattern languages using queries - Satoshi Matsumoto and Ayumi Shinohara |
| | | Learning formulae from elementary facts - Jānis Bārzdiņs, Rīsiņs Freivalds and Carl H. Smith |
| | | Decision tree induction based on efficient tree restructuring - Paul E. Utgoff, Neil C. Berkman and Jeffery A. Clouse |
| | | Performance bounds for nonlinear time series prediction - Ron Meir |
| | | Guest Editors' Introduction - Stephen Muggleton and David Page |
| | | Learning with probabilistic representations - Pat Langley, Gregory M. Provan and Padhraic Smyth |
| | | On a Simple Depth-First Search Strategy for Exploring Unknown Graphs - Stephen Kwek |
| | | First Order Regression - Aram Karaliccaron and Ivan Bratko |
| | | Derandomized learning of Boolean functions - Meera Sitharam and Timothy Straney |
| | | A comparative study of inductive logic programming methods for software fault prediction - William W. Cohen and Prem Devanbu |
| | | Learning simple deterministic finite-memory automata - Hiroshi Sakamoto |
| | | Preventing Overfitting of cross-validation data - Andrew Y. Ng |
| | | Information theory in probability, statistics, learning, and neural nets - Andrew R. Barron |
| | | Efficient feature selection in conceptual clustering - Mark Devaney and Ashwin Ram |
| | | Learning noisy perceptrons by a perceptron in polynomial time - Edith Cohen |
| | | Learning approximately regular languages with reversible languages - Satoshi Kobayashi and Takashi Yokomori |
| | | Bounds on the Number of Examples Needed for Learning Functions - Hans Ulrich Simon |
| | | What makes derivational analogy work: an experience report using APU - Sanjay Bhansali and Mehdi T. Harandi |
| | | Asymmetric Team Learning - Kalvis Aps\=ıtis, Rīsiņš Freivalds and Carl H. Smith |
| | | Learning under persistent drift - Yoav Freund and Yishay Mansour |
| | | On learning branching programs and small depth circuits - Francesco Bergadano, Nader H. Bshouty, Christino Tamon and Stefano Varricchio |
| | | Adaptive probabilistic networks with hidden variables - John Binder, Daphne Koller, Stuart Russell and Keiji Kanazawa |
| | | Hierarchical explanation-based reinforcement learning - Prasad Tadepalli and Thomas G. Dietterich |
| | | Factorial hidden Markov models - Zoubin Ghahramani and Michael I. Jordan |
| | | On learning disjunctions of zero-one threshold functions with queries - Tibor Hegedűs and Piotr Indyk |
| | | Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line - Susanne Kaufmann and Frank Stephan |
| | | Inferability of recursive real-valued functions - Eiju Hirowatari and Setsuo Arikawa |
| | | Stability Analysis of Learning Algorithms for Blind Source Separation - Shun-ichi Amari, Tian-ping Chen and Andrzej Cichocki |
| | | Foreword - T. Zeugmann |
| | | Learning of Associative Memory Networks Based upon Cone-Like Domains of Attraction - Koichi Niijima |
| | | Monotone extensions of Boolean data sets - Endre Boros, Toshihide Ibaraki and Kazuhisa Makino |
| | | Probabilistic linear tree - João Gama |
| | | Learning acyclic first-order Horn sentences from entailment - Hiroki Arimura |
| | | A Survey of Inductive Inference with an Emphasis on Learning via Queries - William Gasarch and Carl H. Smith |
| | | The Binary Exponentiated Gradient Algorithm for Learning Linear Functions - Tom Bylander |
| | | Pruning adaptive boosting - Dragos D. Margineantu and Thomas G. Dietterich |
| | | Exploring the decision forest: an empirical investigation of Occam's razor in decision tree induction - Patrick M. Murphy and Michael J. Pazzani |
| | | Probability theory for the Brier game - V. Vovk |
| | | Elementary formal systems, intrinsic complexity, and procrastination - Sanjay Jain and Arun Sharma |
| | | Learning with Maximum-Entropy Distributions - Yishay Mansour and Mariano Schain |
| | | Pessimistic decision tree pruning based on tree size - Yishay Mansour |
| | | Fast perceptual learning of motion in humans and neural networks - Lucia M. Vaina, Venkrataraman Sundareswaran and John G. Harris |
| | | A model of interactive teaching - H. David Mathias |
| | | PAC learning from general examples - Paul Fischer, Klaus-Uwe Höffgen and Hanno Lefmann |
| | | The effective size of a neural network: A principal component approach - David W. Opitz |
| | | Robot learning from demonstration - Christopher G. Atkeson and Stefan Schaal |
| | | Guest Editor's Introduction - Philip M. Long |
| | | Exponentiated gradient methods for reinforcement learning - Doina Precup and Richard S. Sutton |
| | | Functional models for regression tree leaves - Luís Torgo |
| | | Program Error Detection/Correction: Turning PAC Learning into PERFECT Learning - Manuel Blum |
| | | Abnormal data points in the data set: an algorithm for robust neural net regression - Yong Liu |
| | | An exact probability metric for decision tree splitting and stopping - J. Kent Martin |
| | | On the Complexity of Learning for a Spiking Neuron - Wolfgang Maass and Michael Schmitt |
| | | Generalizations in Typed Equational Programming and Their Application to Learning Functions - A. Ishino and A. Yamamoto |
| | | The canonical distortion measure for vector quantization and function approximation - Jonathan Baxter |
| | | Learning verb translation rules from ambiguous examples and a large semantic hierarchy - Hussein Almuallim, Yasuhiro Akiba, Takefumi Yamazaki and Shigeo Kaneda |
| | | A Comparison of New and Old Algorithms for a Mixture Estimation Problem - David P. Helmbold, Robert E. Schapire andYoram Singer and Manfred K. Warmuth |
| | | On-line evaluation and prediction using linear functions - Philip M. Long |
| | | Exact learning via teaching assistants - V. Arvind and N. V. Vinodchandran |
| | | Feature engineering and classifier selection: A case study in Venusian volcano detection - Lars Asker and Richard Maclin |
| | | A comparative study on feature selection in text categorization - Yiming Yang and Jan O. Pedersen |
| | | Learning Markov chains with variable length memory from noisy output - Dana Angluin and Miklós Csűrös |
| | | General Convergence Results for Linear Discriminant Updates - Adam J. Grove, Nick Littlestone and Dale Schuurmans |
| | | Self-improving factory simulation using continuous-time average-reward reinforcement learning - Sridhar Mahadevan, Nicholas Marchalleck, Tapas K. Das and Abhijit Gosavi |
| | | A Microscopic Study of Minimum Entropy Search in Learning Decomposable Markov Networks - Y. Xiang, S. K. M. Wong and N. Cercone |
| | | Optimal attribute-efficient learning of disjunction, parity, and threshold functions - Ryuhei Uehara, Kensei Tsuchida and Ingo Wegener |
| | | An efficient membership-query algorithm for learning DNF with respect to the uniform distribution - Jeffrey C. Jackson |
| | | Learning from incomplete boundary queries using split graphs and hypergraphs - Robert H. Sloan and György Turán |
| | | Analysis of two gradient-based algorithms for on-line regression - Nicolò Cesa-Bianchi |
| | | Learning belief networks in the presence of missing values and hidden variables - Nir Friedman |
| | | An Experimental and Theoretical Comparison of Model Selection Methods - Michael Kearns, Yishay Mansour, Andrew Y. Ng and Dana Ron |
| | | Generalization of Clauses Relative to a Theory - Peter Idestam-Almquist |
| | | Efficient learning of regular expressions from approximate examples - Alvis Brāzma |
| | | PAC learning using Nadaraya-Watson estimator based on orthonormal systems - Hongzhu Qiao, Nageswara S. V. Rao and V. Protopopescu |
| | | The effects of training set size on decision tree complexity - Tim Oates and David Jensen |
| | | On fast and simple algorithms for finding maximal subarrays and applications in learning theory - Andreas Birkendorf |
| | | Explanation-based learning and reinforcement learning: a unified view - Thomas G. Dietterich and Nicholas S. Flann |
| | | Mixture models for learning from incomplete data - Zoubin Ghahramani and Michael I. Jordan |
| | | Polynomial time inductive inference of regular term tree languages from positive data - Satoshi Matsumoto, Yukiko Hayashi and Takayoshi Shoudai |
| | | Team learning as a game - Andris Ambainis, Kalvis Aps\=ıtis, Rīsiņš Freivalds, William Gasarch and Carl H. Smith |
| | | A PAC Analysis of a Bayesian Estimator - John Shawe-Taylor and Robert C. Williamson |
| | | Generalized Notions of Mind Change Complexity - Arun Sharma, Frank Stephan and Yuri Ventsov |
| | | Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space - Shumeet Baluja and Scott Davies |
| | | Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks - Marek Karpinski and Angus Macintyre |
| | | Knowing what doesn't matter: exploiting the omission of irrelevant data - Russell Greiner, Adam J. Grove and Alexander Kogan |
| | | Learning goal-decomposition rules using exercises - Chandra Reddy and Prasad Tadepalli |
| | | Approximation and Learning of Convex Superpositions - Leonid Gurvits and Pascal Koiran |
| | | FINite Learning Capabilities and Their Limits - Robert Daley and Bala Kalyanasundaram |
| | | Proceedings of the Tenth Annual Conference on Computational Learning Theory - Yaov Freund and Robert Shapire |
| | | Oracles in Sigmap2 are sufficient for exact learning - Johannes Köbler and Wolfgang Lindner |
| | | A minimax lower bound for empirical quantizer design - Peter Bartlett, Tamás Linder and Gábor Lugosi |
| | | Learning symbolic prototypes - Piew Datta and Dennis Kibler |
| | | Pruning Algorithms for Rule Learning - Fürnkranz Johannes |
| | | Recurrent neural networks with continuous topology adaptation, Kalman filter bsed training - Dragan Obradovic |
| | | Learning to Classify Incomplete Examples - Dale Schuurmans and Russell Greiner |
| | | Exactly Learning Automata of Small Cover Time - Dana Ron and Ronitt Rubinfeld |
| | | Coping with uncertainty in map learning - Kenneth Basye, Thomas Dean and Jeffrey Scott Vitter |
| | | Scale-sensitive dimensions, uniform convergence, and learnability - Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi and David Haussler |
| | | Improving minority class prediction using case-specific feature weights - Claire Cardie and Nicholas Howe |
| | | Boosting the margin: a new explanation for the effectiveness of voting methods - Robert E. Schapire, Yoav Freund, Peter Bartlett and Wee Sun Lee |
| | | Nearly tight bounds on the learnability of evolution - Andris Ambainis, Richard Desper, Martin Farach and Sampath Kannan |
| | | Predicting nearly as well as the best pruning of a decision tree - D. P. Helmbold and R. E. Schapire |
| | | Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher - Shai Ben-David and Michael Lindenbaum |
| | | The Discovery of Algorithmic Probability - Ray J. Solomonoff |
| | | Bounds for the Computational Power and Learning Complexity of Analog Neural Nets - Wolfgang Maass |
| | | Characterisitc Sets for Polynomial Grammatical Inference - Colin de la Higuera |
| | | On Case-Based Learnabilty of Languages - C. Globig, K. P. Jantke, S. Lange and Y. Sakakibara |
| | | Synthesizing noise-tolerant language learners - John Case, Sanjay Jain and Arun Sharma |
| | | How to use expert advice - Nicolò Cesa-Bianchi, Yaov Freund, David Haussler, David P. Helmbold, Robert E. Schapire and Manfred K. Warmuth |
| | | Noise-tolerant Efficient Inductive Synthesis of Regular Expressions from Good Examples - A. Brāzma and Čerāns |
| | | The Maximum Latency and Identification of Positive Boolean Functions - Kazuhisa Makino and Toshihide Ibaraki |
| | | A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces - Leonid Gurvits |
| | | Algorithmic stability and sanity-check bounds for leave-one-out cross-validation - Michael Kearns and Dana Ron |
| | | Imposing bounds on the number of categories for incremental concept formation - Leon Shklar and Haym Hirsh |
| | | Option decision trees with majority votes - Ron Kohavi and Clayton Kunz |
| | | Vapnik-Chervonenkis dimension of recurrent neural networks - Pascal Koiran and Eduardo D. Sontag |
| | | Teachers, Learners and Black Boxes - Dana Angluin and Mārtiņš Krikis |
| | | A framework for incremental learning of logic programs - M. R. K. Krishna Rao |
| | | Why experimentation can be better than Perfect Guidance - Tobias Scheffer, Russell Greiner and Christian Darken |
| | | PAL: A Pattern and dash;Based First and dash;Order Inductive System - Eduardo F. Morales |
| | | Monotonic and dual-monotonic probabilistic language learning of indexed families with high probability - Léa Meyer |
| | | Robust learning with infinite additional information - Susanne Kaufmann and Frank Stephan |
| | | Learning nested differences in the presence of malicious noise - Peter Auer |
| | | The Structure of Intrinsic Complexity of Learning - Sanjay Jain and Arun Sharma |
| | | An adaptation of Relief for attribute estimation in regression - Marko Robnik-ťikonja and Igor Kononenko |
| | | Addressing the curse of imbalanced training sets: one-sided selection - Miroslav Kubat and Stan Matwin |
| | | Dense shattering and teaching dimensions for differentiable families - A. Kowalczyk |
| | | Machine learning by function decomposition - Blaž Zupan, Marko Bohanec, Ivan Bratko and Janez Demšar |
| | | Learning and Updating of Uncertainty in Dirichlet Models - Enrique Castillo, Ali S. Hadi and Cristina Solares |
| | | Characterizing Rational Versus Exponential Learning Curves - Dale Schuurmans |
| | | Learning disjunctions of features - Stephen Kwek |
| | | On the decomposition of polychotomies into dichotomies - Eddy Mayoraz and Miguel Moreira |
| | | Noisy inference and oracles - Frank Stephan |
| | | Strong monotonic and set-driven inductive inference - Sanjay Jain |
| | | PAC learning with constant-partition classification noise and applications to decision tree induction - Scott Decatur |
| | | Exact Learning of Formulas in Parallel - Nader H. Bshouty |
| | | Learning when to trust which experts - David Helmbold, Stephen Kwek and Leonard Pitt |
| | | Improving regressors using boosting techniques - Harris Drucker |
| | | Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables - David Maxwell Chickering and David Heckerman |
| | | Fast Distribution-Specific Learning - Dale Schuurmans and Russell Greiner |
| | | An efficient exact learning algorithm for ordered binary decision diagrams - Atsuyoshi Nakamura |
| | | Reinforcement learning in POMDPs with function approximation - Hajime Kimura, Kazuteru Miyazaki and Shigenobu Kobayashi |
| | | On learning the neural network architecture: a case study - Mostefa Golea |
| | | The discriminative power of a dynamical model neuron - Anthony M. Zador and Barak A. Pearlmutter |
| | | Recent advances of grammatical inference - Yasubumi Sakakibara |
| | | Knowledge acquisition from examples via multiple models - Pedro Domingos |
| | | Learning Logic Programs by using the Product Homomorphism Method - Tamás Horváth, Robert H. Sloan and György Turán |
| | | Inferring a system from examples with time passage - Yasuhito Mukouchi |
| | | Generating all Maximal Independent Sets of Bounded-degree Hypergraphs - Nina Mishra and Leonard Pitt |
| | | Randomized hypotheses and minimum disagreement hypotheses for learning with noise - Nicolò Cesa-Bianchi, Paul Fischer, Eli Shamir and Hans Ulrich Simon |
| | | N-learners problem: system of PAC learners - Nageswara S. V. Rao and E. M. Oblow |
| | | Predicting protein secondary structure using stochastic tree grammars - Naoki Abe and Hiroshi Mamitsuka |
| | | Learning Probabilistically Consistent Linear Threshold Functions - Tom Bylander |
| | | Control structures in hypothesis spaces: the influence on learning - John Case, Sanjay Jain and Mandayam Suraj |
| | | Learning orthogonal F-Horn formulas - Eiji Takimoto, Akira Miyashiro, Akira Maruoka and Yoshifumi Sakai |
| | | Learning of r.e. languages from good examples - Sanjay Jain, Steffen Lange and Jochen Nessel |
| | | Inductive Program Synthesis for Therapy Plan Generation - O. Arnold and K. P. Jantke |
| | | A simple algorithm for predicting nearly as well as the best pruning labeled with the best prediction values of a decision tree - Eiji Takimoto, Ken'ichi Hirai and Akira Maruoka |
| | | Representing Probabilistic Rules with Networks of Gaussian Basis Functions - Volker Tresp, Jürgen Hollatz and Subutai Ahmad |
| | | Machine Learning - Tom M. Mitchell |
| | | Learning Recursive Functions from Approximations - John Case, Susanne Kaufmann, Efim B. Kinber and Martin Kummer |
| | | Kolmogorov numberings and minimal identification - Rusins Freivalds and Sanjay Jain |
| | | Predicting multiprocessor memory access patterns with learning models - M. F. Sakr, S. P. Levitan, D. M. Chiarulli, B. G. Horne and C. L. Giles |
| | | Learning Qualitative Models of Dynamic Systems - David T. Hau and Enrico W. Coiera |
| | | Scaling to domains with irrelevant features - Patrick Langley and Stephanie Sage |
| | | Learning deterministic even linear languages from positive examples - Takeshi Koshiba, Erkki Mäkinen and Yuji Takada |
| | | Integrating feature construction with multiple classifiers in decision tree induction - Ricardo Vilalta and Larry Rendell |
| | | Hierarchically classifying documents using very few words - Daphne Koller and Mehran Sahami |
| | | Generalization of the PAC-model for learning with partial information - Joel Ratsaby and Vitaly Maiorov |
| | | Some Label Efficient Learning Results - David Helmbold and Sandra Panizza |
| | | Learning monotone term decision lists - David Guijarro, Victor Lavin and Vijay Raghavan |
| | | Automatic rule acquisition for spelling correction - Lidia Mangu and Eric Brill |
| | | FONN: Combining first order logic with connectionist learning - Marco Botta, Attilo Giordana and Roberto Piola |
| | | Partial Occam's razor and its applications - Carlos Domingo, Tatsuie Tsukiji and Osamu Watanabe |
| | | Effects of Kolmogorov complexity present in inductive inference as well - Andris Ambainis, Kalvis Aps\=ıtis, Cristian Calude, Rīsiņš Freivalds, Marek Karpinski, Tomas Larfeldt, Iveta Sala and Juris Smotrovs |
| | | Inferring Answers to Queries - William I. Gasarch and Andrew C. Y. Lee |
| | | CHILD: a first step towards continual learning - Mark B. Ring |
| | | Stacking bagged and dagged models - Kai Ming Ting and Ian H. Witten |
| | | PAC learning under helpful distributions - François Denis and Rémi Gilleron |
| | | On the Classification of Computable Languages - John Case, Efim Kinber, Arun Sharma and Frank Stephan |
| | | Agnostic learning of geometric patterns - Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott |
| | | On exploiting knowledge and concept use in learning theory - Leonard Pitt |
| | | Initializing neural networks using decision trees - Arunava Banerji |
| October | | Algorithmic Learning Theory, 8th International Workshop, ALT '97, Sendai, Japan, October 1997, Proceedings - Ming Li and Akira Maruoka |
| December | | Scientific discovery based on belief revision - Eric Martin and Daniel N. Osherson |
