| 1997 | |  | Preventing Overfitting of cross-validation data - Andrew Y. Ng |
| | | Representing Probabilistic Rules with Networks of Gaussian Basis Functions - Tresp Volker, Hollatz Jrgen and Subutai Ahmad |
| | | Improving minority class prediction using case-specific feature weights - Claire Cardie and Nicholas Howe |
| | | Learning Markov chains with variable length memory from noisy output - Dana Angluin and Miklós Csűrös |
| | | Learning Distributions from Random Walks - Funda Ergün, S. Ravi Kumar and Ronitt Rubinfeld |
| | | Linear algebraic proofs of VC-dimension based inequalities - Leonid Gurvits |
| | | Functional models for regression tree leaves - Luís Torgo |
| | | A comparative study on feature selection in text categorization - Yiming Yang and Jan O. Pedersen |
| | | Control structures in hypothesis spaces: the influence on learning - John Case, Sanjay Jain and Mandayam Suraj |
| | | Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line - Susanne Kaufmann and Frank Stephan |
| | | Derandomizing stochastic prediction strategies - V. Vovk |
| | | Boosting the margin: a new explanation for the effectiveness of voting methods - Robert E. Schapire, Yoav Freund, Peter Bartlett and Wee Sun Lee |
| | | Addressing the curse of imbalanced training sets: one-sided selection - Miroslav Kubat and Stan Matwin |
| | | Learning boxes in high dimension - Amos Beimel and Eyal Kushilevitz |
| | | Instance pruning techniques - D. Randall Wilson and Tony R. Martinez |
| | | A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization - Thorsten Joachims |
| | | Pessimistic decision tree pruning based on tree size - Yishay Mansour |
| | | Learning From Examples With Unspecified Attribute Values - Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott |
| | | Learning formulae from elementary facts - Jānis Bārzdiņs, Rīsiņs Freivalds and Carl H. Smith |
| | | Automatic rule acquisition for spelling correction - Lidia Mangu and Eric Brill |
| | | Learning belief networks in the presence of missing values and hidden variables - Nir Friedman |
| | | On-line Learning and the Metrical Task System Problem - Avrim Blum and Carl Burch |
| | | Confidence estimates of classification accuracy on new examples - John Shawe-Taylor |
| | | FONN: Combining first order logic with connectionist learning - Marco Botta, Attilo Giordana and Roberto Piola |
| | | A PAC Analysis of a Bayesian Estimator - John Shawe-Taylor and Robert C. Williamson |
| | | Knowledge acquisition from examples via multiple models - Pedro Domingos |
| | | A <I>Microscopic</I> Study of Minimum Entropy Search in Learning Decomposable Markov Networks - Y. Xiang, S. K. M. Wong and N. Cercone |
| | | On the Complexity of Learning for a Spiking Neuron - Wolfgang Maass and Michael Schmitt |
| | | Exact Learning of Formulas in Parallel - Nader H. Bshouty |
| | | Learning Qualitative Models of Dynamic Systems - David T. Hau and Enrico W. Coiera |
| | | Guest Editor’s Introduction - Philip M. Long |
| | | Learning string edit distance - Eric Sven Ristad and Peter N. Yianilos |
| | | Structural measures for games and process control in the branch learning model - Matthias Ott and Frank Stephan |
| | | On learning branching programs and small depth circuits - Francesco Bergadano, Nader H. Bshouty, Christino Tamon and Stefano Varricchio |
| | | Information theory in probability, statistics, learning, and neural nets - Andrew R. Barron |
| | | Optimal attribute-efficient learning of disjunction, parity, and threshold functions - Ryuhei Uehara, Kensei Tsuchida and Ingo Wegener |
| | | Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain - Avrim Blum |
| | | FINite Learning Capabilities and Their Limits - Robert Daley and Bala Kalyanasundaram |
| | | Some Label Efficient Learning Results - David Helmbold and Sandra Panizza |
| | | Universal portfolios with and without transaction costs - Avrim Blum and Adam Kalai |
| | | Learning under persistent drift - Yoav Freund and Yishay Mansour |
| | | An efficient extension to mixture techniques for prediction and decision trees - Fernando Pereira and Yoram Singer |
| | | Dense shattering and teaching dimensions for differentiable families - A. Kowalczyk |
| | | Generalized Notions of Mind Change Complexity - Arun Sharma, Frank Stephan and Yuri Ventsov |
| | | An adaptation of Relief for attribute estimation in regression - Marko Robnik-ťikonja and Igor Kononenko |
| | | Learning matrix functions over rings - Nader H. Bshouty, Christino Tamon and David K. Wilson |
| | | A minimax lower bound for empirical quantizer design - Peter Bartlett, Tamás Linder and Gábor Lugosi |
| | | Feature engineering and classifier selection: A case study in Venusian volcano detection - Lars Asker and Richard Maclin |
| | | Ordinal mind change complexity of language identification - Andris Ambainis, Sanjay Jain and Arun Sharma |
| | | PAC learning with constant-partition classification noise and applications to decision tree induction - Scott Decatur |
| | | Agnostic learning of geometric patterns - Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott |
| | | A brief look at some machine learning problems in genomics - David Haussler |
| | | Estimation of time-varying parameters in statistical models; an optimization approach - Dimitris Bertsimas, David Gamarnik and John N. Tsitsiklis |
| | | Vapnik-Chervonenkis dimension of recurrent neural networks - Pascal Koiran and Eduardo D. Sontag |
| | | A comparative study of inductive logic programming methods for software fault prediction - William W. Cohen and Prem Devanbu |
| | | An Experimental and Theoretical Comparison of Model Selection Methods - Michael Kearns, Yishay Mansour, Andrew Y. Ng and Dana Ron |
| | | Computational Sample Complexity - Scott Decatur, Oded Goldreich and Dana Ron |
| | | Generalization of Clauses Relative to a Theory - Peter Idestam-Almquist |
| | | Probabilistic linear tree - João Gama |
| | | Pruning adaptive boosting - Dragos D. Margineantu and Thomas G. Dietterich |
| | | Algorithmic stability and sanity-check bounds for leave-one-out cross-validation - Michael Kearns and Dana Ron |
| | | Using output codes to boost multiclass learning problems - Robert E. Schapire |
| | | A Comparison of New and Old Algorithms for a Mixture Estimation Problem - David P. Helmbold, Robert E. Schapire andYoram Singer and Manfred K. Warmuth |
| | | A result relating convex n-widths to covering numbers with some applications to neural networks - Jonathan Baxter and Peter Bartlett |
| | | ARACHNID: Adaptive retrieval agents choosing heuristic neighborhoods for information discovery - Filippo Menczer |
| | | Robot learning from demonstration - Christopher G. Atkeson and Stefan Schaal |
| | | Characterisitc Sets for Polynomial Grammatical Inference - Colin de la Higuear |
| | | Declarative bias in equation discovery - Ljupčo Todorovski and Sašo Džeroski |
| | | Reinforcement learning in POMDPs with function approximation - Hajime Kimura, Kazuteru Miyazaki and Shigenobu Kobayashi |
| | | Learning monotone term decision lists - David Guijarro, Victor Lavin and Vijay Raghavan |
| | | Fast Distribution-Specific Learning - Dale Schuurmans and Russell Greiner |
| | | A dichotomy theorem for learning quantified Boolean formulas - Victor Dalmau |
| | | Learning symbolic prototypes - Piew Datta and Dennis Kibler |
| | | Hierarchically classifying documents using very few words - Daphne Koller and Mehran Sahami |
| | | On-line evaluation and prediction using linear functions - Philip M. Long |
| | | The effective size of a neural network: A principal component approach - David W. Opitz |
| | | Monotonic and dual-monotonic probabilistic language learning of indexed families with high probability - Léa Meyer |
| | | Hierarchical explanation-based reinforcement learning - Prasad Tadepalli and Thomas G. Dietterich |
| | | Stacking bagged and dagged models - Kai Ming Ting and Ian H. Witten |
| | | Predicting multiprocessor memory access patterns with learning models - M. F. Sakr, S. P. Levitan, D. M. Chiarulli, B. G. Horne and C. L. Giles |
| | | The effects of training set size on decision tree complexity - Tim Oates and David Jensen |
| | | Distributed Cooperative Bayesian Learning Strategies - Kenji Yamanishi |
| | | PAL: A Pattern and dash;Based First and dash;Order Inductive System - Eduardo F. Morales |
| | | Inferring Answers to Queries - William I. Gasarch and Andrew C. Y. Lee |
| | | Improving regressors using boosting techniques - Harris Drucker |
| | | Integrating feature construction with multiple classifiers in decision tree induction - Ricardo Vilalta and Larry Rendell |
| | | On the decomposition of polychotomies into dichotomies - Eddy Mayoraz and Miguel Moreira |
| | | Learning pattern languages using queries - Satoshi Matsumoto and Ayumi Shinohara |
| | | Learning when to trust which experts - David Helmbold, Stephen Kwek and Leonard Pitt |
| | | Option decision trees with majority votes - Ron Kohavi and Clayton Kunz |
| | | Randomized hypotheses and minimum disagreement hypotheses for learning with noise - Nicolò Cesa-Bianchi, Paul Fischer, Eli Shamir and Hans Ulrich Simon |
| | | Asymmetric Team Learning - Kalvis Apsītis, Rīsiņš Freivalds and Carl H. Smith |
| | | Pruning Algorithms for Rule Learning - Frnkranz Johannes |
| | | Learning to Classify Incomplete Examples - Dale Schuurmans and Russell Greiner |
| | | Machine learning by function decomposition - Blaž Zupan, Marko Bohanec, Ivan Bratko and Janez Demšar |
| | | First Order Regression - Aram Karaliccaron and Ivan Bratko |
| | | On fast and simple algorithms for finding maximal subarrays and applications in learning theory - Andreas Birkendorf |
| | | Learning with Maximum-Entropy Distributions - Yishay Mansour and Mariano Schain |
| | | Closedness properties in team learning of recursive functions - Juris Smotrovs |
| | | PAC Adaptive Control of Linear Systems - Claude-Nicolas Fiechter |
| | | Robust learning with infinite additional information - Susanne Kaufmann and Frank Stephan |
| | | On learning from multi-instance examples: Empirical evaluation of a theoretical approach - Peter Auer |
| | | Characterizing the generalization performance of model selection strategies - Dale Schuurmans, Lyle H. Ungar and Dean P. Foster |
| | | Clausal Discovery - Luc De Raedt and Luc Dehaspe |
| | | Learning and Updating of Uncertainty in Dirichlet Models - Enrque Castillo, Ali S. Hadi and Cristina Solares |
| | | Efficient locally weighted polynomial regression predictions - Andrew W. Moore, Jeff Schneider and Kan Deng |
| | | Learning Probabilistically Consistent Linear Threshold Functions - Tom Bylander |
| | | Learning from incomplete boundary queries using split graphs and hypergraphs - Robert H. Sloan and György Turán |
| | | Learning Logic Programs by using the Product Homomorphism Method - Tamás Horváth, Robert H. Sloan and György Turán |
| | | Learning nearly monotone k-term DNF - Jorge Castro, David Guijarro and Victor Lavin |
| | | Self-improving factory simulation using continuous-time average-reward reinforcement learning - Sridhar Mahadevan, Nicholas Marchalleck, Tapas K. Das and Abhijit Gosavi |
| | | Generalization of the PAC-model for learning with partial information - Joel Ratsaby and Vitaly Maiorov |
| | | Guest Editors’ Introduction - Stephen Muggleton and David Page |
| | | Efficient feature selection in conceptual clustering - Mark Devaney and Ashwin Ram |
| | | The canonical distortion measure for vector quantization and function approximation - Jonathan Baxter |
| | | A Bayesian approach to model learning in non-Markovian environments - Nobuo Suematsu, Akira Hayashi and Shigang Li |
| | | Analysis of two gradient-based algorithms for on-line regression - Nicolò Cesa-Bianchi |
| | | Learning goal-decomposition rules using exercises - Chandra Reddy and Prasad Tadepalli |
| | | Generating all Maximal Independent Sets of Bounded-degree Hypergraphs - Nina Mishra and Leonard Pitt |
| | | General Convergence Results for Linear Discriminant Updates - Adam J. Grove, Nick Littlestone and Dale Schuurmans |
| | | Why experimentation can be better than Perfect Guidance - Tobias Scheffer, Russell Greiner and Christian Darken |
| | | Sample compression, learnability, and the Vapnik-Chervonenkis dimension - Manfred Warmuth |
| | | Exactly Learning Automata of Small Cover Time - Dana Ron and Ronitt Rubinfeld |
| | | Exponentiated gradient methods for reinforcement learning - Doina Precup and Richard S. Sutton |
| | | Performance bounds for nonlinear time series prediction - Ron Meir |
| | | Teachers, Learners and Black Boxes - Dana Angluin and Mārtiņš Krikis |
| | | Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space - Shumeet Baluja and Scott Davies |
| | | The Binary Exponentiated Gradient Algorithm for Learning Linear Functions - Tom Bylander |
