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* |