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 ***Sigma*^{p}_{2} 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* |