1995 | | | **On approximately identifying concept classes in the limit** - *Satoshi Kobayashi and Takashi Yokomori* |

| | | **A Guided Tour Across the Boundaries of Learning Recursive Languages** - *T. Zeugmann and S. Lange* |

| | | **Corrigendum for: Learnability of description logics** - *William W. Cohen and Haym Hirsh* |

| | | **Technical and Scientific Issues of KDD (or: Is KDD a Science?)** - *Yves Kodratoff* |

| | | **Piecemeal Learning of an Unknown Environment** - *Margrit Betke, Ronald L. Rivest and Mona Singh* |

| | | **Practical PAC Learning** - *Dale Schuurmans and Russell Greiner* |

| | | **Removing the genetics from the standard genetic algorithm** - *Shumeet Baluja and Rich Caruana* |

| | | **Learnability of Kolmogorov-easy circuit expressions via queries** - *José L. Balcázar, Harry Buhrman and Montserrat Hermo* |

| | | **Inductive Synthesis of Rewrite Programs** - *Ulf Goldammer* |

| | | **Inductive learning of reactive action models** - *Scott Benson* |

| | | **On-line learning of binary lexical relations using two-dimensional weighted majority algorithms** - *Naoki Abe, Hang Li and Atsuyoshi Nakamura* |

| | | **Learning and Consistency** - *R. Wiehagen and T. Zeugmann* |

| | | **Learning strongly deterministic even linear languages from positive examples** - *Takeshi Koshiba, Erkki Mäkinen and Yuji Takada* |

| | | **A learning theoretic characterization of classes of recursive functions** - *Martin Kummer* |

| | | **Information Geometry of the EM and em Algorithms for Neural Networks** - *Shun-ichi Amari* |

| | | **Language Learning from Membership Queries and Characteristic Examples** - *Hiroshi Sakamoto* |

| | | **Bounding the Vapnik-Chervonenkis dimension of concept classes parametrized by real numbers** - *Paul W. Goldberg and Mark R. Jerrum* |

| | | **Optimization problem in inductive inference** - *A. Ambainis* |

| | | **Additive versus exponentiated gradient updates for linear prediction** - *Jyrki Kivinen and Manfred K. Warmuth* |

| | | **Learning from good examples** - *R. Freivalds, E. B. Kinber and R. Wiehagen* |

| | | **Breaking the Probability 1/2 Barrier in FIN-type Learning** - *R. Daley, B. Kalyanasundaram and M. Velauthapillai* |

| | | **On learning decision trees with large output domains** - *Nader H. Bshouty, Christino Tamon and David K. Wilson* |

| | | **Function learning from interpolation** - *Martin Anthony and Peter Bartlett* |

| | | **Discovering solutions with low Kolmogorov complexity and high generalization capability** - *Jürgen Schmidhuber* |

| | | **Case-Based Representation and Learning of Pattern Languages** - *Klaus P. Jantke and Steffen Lange* |

| | | **Efficient Learning of Real Time One-Counter Automata** - *Amr F. Fahmy and Robert S. Roos* |

| | | **Editors' Introduction** - *Klaus P. Jantke, Takeshi Shinohara and T. Zeugmann* |

| | | **Is the pocket algorithm optimal?** - *Marco Muselli* |

| | | **A note on VC-dimension and measure of sets of reals** - *Shai Ben-David and Leonid Gurvits* |

| | | **Ant-Q:a reinforcement learning approach to the traveling salesman problem** - *Luca M. Gambardella and Marco Dorigo* |

| | | **An empirical investigation of brute force to choose features, smoothers and function approximators** - *Andrew W. Moore, Daniel J. Hill and Michael P. Johnson* |

| | | **Monotonicity maintenance in information-theoretic machine learning algorithms** - *Arie Ben-David* |

| | | **Trading Monotonicity Demands versus Mind Changes** - *Steffen Lange and Thomas Zeugmann* |

| | | **Guest Editor's Introduction** - *Sally A. Goldman* |

| | | *varepsilon*-approximations of k-label spaces - *Susumu Hasegawa, Hiroshi Imai and Masaki Ishiguro* |

| | | **Learning via queries and oracles** - *Frank Stephan* |

| | | **Learning by extended statistical queries and its relation to PAC learning** - *Eli Shamir and Clara Schwartzman* |

| | | **Proceedings of the Eighth Annual Conference on Computational Learning Theory** - *Wolfgang Maass* |

| | | **For every generalization action is there really an equal and opposite reaction? Analysis of the conservation law for generalization performance** - *R. Bharat Rao, Diana Gordon and William Spears* |

| | | **A typed ***lambda*-calculus for proving-by-example and bottom-up generalization procedure - *Masami Hagiya* |

| | | **On Aggregating Teams of Learning Machines** - *Sanjay Jain and Arun Sharma* |

| | | **Simple PAC learning of simple decision lists** - *Jorge Castro and José L. Balcázar* |

| | | **On identification by teams and probabilistic machines** - *Sanjay Jain and Arun Sharma* |

| | | **Finite Identification of Functions by Teams with Success Ratio ***frac*12 and Above - *Sanjay Jain, Arun Sharma and Mahendran Velauthapillai* |

| | | **On a Question about Learning Nearly Minimal Programs** - *S. Jain* |

| | | **Cognitive Computation (Extended Abstract)** - *Leslie G. Valiant* |

| | | **Cross-validation and modal theories** - *Timothy L. Bailey and Charles Elkan* |

| | | **Learning by observation and practice: an incremental approach for planning operator acquisition** - *Xuemei Wang* |

| | | **Theory and applications of agnostic PAC-learning with small decision trees** - *Peter Auer, Robert C. Holte and Wolfgang Maass* |

| | | **Multivariate decision trees** - *Carla E. Brodley and Paul E. Utgoff* |

| | | **Learning from a mixture of labeled and unlabeled examples with parametric side information** - *Joel Ratsaby and Santosh S. Venkatesh* |

| | | **Classifying recursive predicates and languages** - *R. Wiehagen, C. H. Smith and T. Zeugmann* |

| | | **Simulating Teams with Many Conjectures** - *Bala Kalyanasundaram and Mahendran Velauthapillai* |

| | | **Learning by Distances** - *S. Ben-David, A. Itai and E. Kushilevitz* |

| | | **An O(n**^{loglog n}) learning algorithm for DNF under the uniform distribution - *Yishay Mansour* |

| | | **Optimal Strategies - Learning from Examples - Boolean Equations** - *Christian Posthoff and Michael Schlosser* |

| | | **Complexity Issues for Vacillatory Function Identification** - *J. Case, S. Jain and A. Sharma* |

| | | **Complexity of network training for classes of neural networks** - *Charles C. Pinter* |

| | | **Automatic speaker recognition: an application of machine learning** - *Brett Squires and Claude Sammut* |

| | | **Learning sparse linear combinations of basis functions over a finite domain** - *Atsuyoshi Nakamura and Shinji Miura* |

| | | **Efficient learning from delayed rewards through symbolic evolution** - *David E. Moriarty and Risto Miikkulainen* |

| | | **More or less efficient agnostic learning of convex polygons** - *Paul Fischer* |

| | | **Learning Formal Languages Based on Control Sets** - *Yuji Takada* |

| | | **Recursive automatic bias selection for classifier construction** - *Carla E. Brodley* |

| | | **Improving model selection by dynamic regularization methods** - *Ferdinand Hergert, William Finnoff and Hans-Georg Zimmermann* |

| | | **The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces** - *Andrew W. Moore and Christopher G. Atkeson* |

| | | **Supervised and unsupervised discretization of continuous features** - *James Dougherty, Ron Kohavi and Mehran Sahami* |

| | | **Inductive Inference of Recurrence-Term Languages from Positive Data** - *Phil Watson* |

| | | **Learning from a population of hypotheses** - *Michael Kearns and H. Sebastian Seung* |

| | | **Learning to model sequences generated by switching distributions** - *Yoav Freund and Dana Ron* |

| | | **General Bounds for Predictive Errors in Supervised Learning** - *Manfred Opper and David Haussler* |

| | | **Comparing several linear-threshold learning algorithms on tasks involving superfluous attributes** - *Nick Littlestone* |

| | | **Inferring finite automata with stochastic output functions and an application to map learning** - *Thomas Dean, Dana Angluin, Kenneth Basye, Sean Engelson, Leslie Kaelbling, Evangelos Kokkevis and Oded Maron* |

| | | **Learning context to disambiguate word senses** - *Ellen M. Voorhees, Claudia Leacock and Geoffrey Towell* |

| | | **Error-correcting output coding corrects bias and variance** - *Eun Bae Kong and Thomas G. Dietterich* |

| | | **Estimating continuous distributions in Bayesian classifiers** - *George H. John and Pat Langley* |

| | | **Generalized teaching dimensions and the query complexity of learning** - *Tibor Hegedüs* |

| | | **Efficient algorithms for learning to play repeated games against computationally bounded adversaries** - *Yoav Freund, Michael Kearns, Yishay Mansour, Dana Ron and Ronitt Rubinfeld* |

| | | **Learning collection fusion strategies for information retrieval** - *Geoffrey Towell, Ellen M. Voorhees, Narendra K. Gupta and Ben Johnson-Laird* |

| | | **Four types of noise in data for PAC Learning** - *R. H. Sloan* |

| | | **Noise-tolerant parallel learning of geometric concepts** - *Nader H. Bshouty, Sally A. Goldman and David H. Mathias* |

| | | **PAC-learnability of constrained nonrecursive logic programs** - *Sašo Džeroski, Stephen Muggleton and Stuart Russell* |

| | | **Characterizations of learnability for classes of {0,***dots*, n}-valued functions - *Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler and Philip M. Long* |

| | | **An Infinite Class of Functions Identifiable Using Minimal Programs in all Kolmogorov Numberings** - *Sanjay Jain* |

| | | **An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms** - *Dietrich Wettschereck and Thomas G. Dietterich* |

| | | **Shifting Vocabulary Bias in Speedup Learning** - *Devika Subramanian* |

| | | **Efficient learning with virtual threshold gates** - *Wolfgang Maass and Manfred K. Warmuth* |

| | | **The query complexity of learning some subclasses of context-free grammars** - *Carlos Domingo and Victor Lavín* |

| | | **A knowledge-based model of geometry learning** - *Geoffrey Towell and Richard Lehrer* |

| | | **Incremental learning of logic programs** - *M. R. K. Krishna Rao* |

| | | **On genetic algorithms** - *Eric B. Baum, Dan Boneh and Charles Garrett* |

| | | **Machine Induction Without Revolutionary Paradigm Shifts** - *John Case, Sanjay Jain and Arun Sharma* |

| | | **TD models: modeling the world at a mixture of time scales** - *Richard S. Sutton* |

| | | **Analysis of the blurring process** - *Yizong Cheng and Zhangyong Wan* |

| | | **Fast effective rule induction** - *William W. Cohen* |

| | | **A note on learning multivariate polynomials under the uniform distribution** - *Nader H. Bshouty* |

| | | **Learning proof heuristics by adapting parameters** - *Matthias Fuchs* |

| | | **On the Bayesian 'Occam factors' argument for Occam's razor** - *David H. Wolpert* |

| | | **An inductive learning approach to prognostic prediction** - *W. Nick Street, O. L. Mangasarian and W. H. Wolberg* |

| | | **The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant** - *Jyrki Kivinen and Manfred K. Warmuth* |

| | | **Modeling Incremental Learning from Positive Data** - *S. Lange and T. Zeugmann* |

| | | **Visualizing high-dimensional structure with the incremental grid-growing neural network** - *Justine Blackmore and Risto Miikkulainen* |

| | | **Predictive Hebbian learning** - *Terrence J. Sejnowski, Peter Dayan and P. Read Montague* |

| | | **How Inductive Inference Strategies Discover Their Errors** - *Rīsiņš Freivalds, Efim B. Kinber and Rolf Wiehagen* |

| | | **On the learnability of Z**_{N}-DNF formulas - *Nader H. Bshouty, Zhixiang Chen, Scott E. Decatur and Steven Homer* |

| | | **Probably Almost Discriminative Learning** - *Kenji Yamanishi* |

| | | **Soft classification, a.k.a. risk estimation, via penalized log likelihood and smoothing spline analysis of variance** - *Grace Wahba, Chong Gu, Yuedong Wang and Richard Chappell* |

| | | **Probabilistic versus Deterministic Memory Limited Learning** - *R. Freivalds, E. B. Kinber and C. H. Smith* |

| | | **The Appropriateness of Predicate Invention as Bias Shift Operation in ILP** - *Irene Stahl* |

| | | **Learning by a population of perceptrons** - *Kukjin Kang and Jong-Hoon Oh* |

| | | **ALECSYS and the AutonoMouse: learning to control a real robot by distributed classifier systems** - *Marco Dorigo* |

| | | **Neural networks for full-scale protein sequence classification: sequence encoding with singular value decomposition** - *Cathy Wu, Michael Berry, Sailaja Shivakumar and Jerry McLarty* |

| | | **Investigating the value of a good input representation** - *Mark W. Craven and Jude W. Shavlik* |

| | | **Recurrent neural networks with time-dependent inputs and outputs** - *Volkmar Sterzing and Bernd Schürmann* |

| | | **Residual algorithms: reinforcement learning with function approximation** - *Leemon Baird* |

| | | **On the optimal capacity of binary neural networks: rigorous combinatorial approaches** - *Jeong Han Kim and James R. Roche* |

| | | **Q-learning for bandit problems** - *Michael O. Duff* |

| | | **Fast learning of k-term DNF formulas with queries** - *Avrim Blum and Stephen Rudich* |

| | | **A comparative evaluation of voting and meta-learning on partitioned data** - *Philip K. Chan and Salvatore J. Stolfo* |

| | | **High accuracy path tracking by neural linearization techniques** - *Stefan Miesbach* |

| | | **From noise-free to noise-tolerant and from on-line to batch learning** - *Norbert Klasner and Hans Ulrich Simon* |

| | | **Sample sizes for sigmoidal neural networks** - *John Shawe-Taylor* |

| | | **On the computational power of neural nets** - *Hava T. Siegelmann and Eduardo D. Sontag* |

| | | **Learning with rare cases and small disjuncts** - *Gary M. Weiss* |

| | | **Inductive inference of functions on the rationals** - *Douglas A. Cenzer and William R. Moser* |

| | | **Compression-based discretization of continuous attributes** - *Bernhard Pfahringer* |

| | | **On learning from noisy and incomplete examples** - *Scott E. Decatur and Rosario Gennaro* |

| | | **Hill climbing beats genetic search on a Boolean circuit problem of Koza's** - *Kevin J. Lang* |

| | | **Retrofitting decision tree classifiers using kernel density estimation** - *Padhraic Smyth, Alexander Gray and Usama M. Fayyad* |

| | | **Learning via Queries, Teams, and Anomalies** - *William Gasarch, Efim Kinber, Mark Pleszkoch, Carl Smith and Thomas Zeugmann* |

| | | **On Pruning and averaging decision trees** - *Jonathan J. Oliver and David J. Hand* |

| | | **Importance-based feature extraction for reinforcement learning** - *David J. Finton and Yu Hen Hu* |

| | | **DNF - if you can't learn 'em, teach 'em: an interactive model of teaching** - *David H. Mathias* |

| | | **Learning internal representations** - *Jonathan Baxter* |

| | | **Support-vector networks** - *Corinna Cortes and Vladimir Vapnik* |

| | | **Learning decision lists and trees with equivalence-queries** - *Hans Ulrich Simon* |

| | | **Language Learning from Texts: Mindchanges, Limited Memory, and Monotonicity** - *Efim Kinber and Frank Stephan* |

| | | **Simple learning algorithms using divide and conquer** - *Nader H. Bshouty* |

| | | **Mutual Information and Bayes Methods for Learning a Distribution** - *David Haussler and Manfred Opper* |

| | | **Application of Kolmogorov Complexity to Inductive Inference with Limited Memory** - *Andris Ambainis* |

| | | **When won't membership queries help?** - *Dana Angluin and Michael Kharitonov* |

| | | **Trading monotonicity demands versus efficiency** - *S. Lange and T. Zeugmann* |

| | | **On learning decision committees** - *Richard Nock and Olivier Gascuel* |

| | | **Committee-based sampling for training probabilistic classifiers** - *Ido Dagan and Sean P. Engelson* |

| | | **Learning policies for partially observable environments: scaling up** - *Michael L. Littman, Anthony R. Cassandra and Leslie Pack Kaelbling* |

| | | **Efficient algorithms for finding multi-way splits for decision trees** - *Truxton Fulton, Simon Kasif and Steven Salzberg* |

| | | **Learning of regular expressions by pattern matching** - *Alvis Brāzma* |

| | | **Bounds on the classification error of the nearest neighbor rule** - *John A. Drakopoulos* |

| | | **Increasing the performance and consistency of classification trees by using the accuracy criterion at the leaves** - *David J. Lubinsky* |

| | | **Criteria for specifying machine complexity in learning** - *Changfeng Wang and Santosh S. Venkatesh* |

| | | **Bounding VC-dimension for neural networks: progress and prospects** - *Marek Karpinski and Angus Macintyre* |

| | | **Book Review: Neural Network Perception for Mobile Robot Guidance by Dean A. Pomerleau. Kluwer Academic Publishers, 1993.** - *Geoffrey Towell* |

| | | **A Bayesian analysis of algorithms for learning finite functions** - *James Cussens* |

| | | **On Weak Learning** - *David P. Helmbold and Manfred K. Warmuth* |

| | | **Encouraging Experimental Results on Learning CNF** - *Raymond J. Mooney* |

| | | **Polynomial bounds for VC dimension of sigmoidal neural networks** - *Marek Karpinski and Angus Macintyre* |

| | | **Inductive Policy: The Pragmatics of Bias Selection** - *John Foster Provost and Bruce G. Buchanan* |

| | | **Simple learning algorithms for decision trees and multivariate polynomials** - *Nader H. Bshouty and Yishay Mansour* |

| | | **Learning to make rent-to-buy decisions with systems applications** - *P. Krishnan, Philip M. Long and Jeffrey Scott Vitter* |

| | | **Learning ordered binary decision diagrams** - *Ricard Gavaldà and David Guijarro* |

| | | **Pac-Learning Recursive Logic Programs: Efficient Algorithms** - *William W. Cohen* |

| | | **Reducing the small disjuncts problem by learning probabilistic concept descriptions** - *Kamal M. Ali and Michael J. Pazzani* |

| | | **Robust trainability of single neurons** - *Klaus-U. Höffgen, Hans-U. Simon and Kevin S. Van Horn* |

| | | **Learning polynomials with queries: the highly noisy case** - *Oded Goldreich, Ronitt Rubinfeld and Madhu Sudan* |

| | | **Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension** - *D. Haussler* |

| | | **Randomized approximate aggregating strategies and their applications to prediction and discrimination** - *Kenji Yamanishi* |

| | | **Unsupervised learning of multiple motifs in biopolymers using expectation maximization** - *Timothy L. Bailey and Charles Elkan* |

| | | **How to use expert advice in the case when actual values of estimated events remain unknown** - *Olga Mitina and Nikolai Vereshchagin* |

| | | **Distilling reliable information from unreliable theories** - *Sean P. Engelson and Moshe Koppel* |

| | | **On learning multiple concepts in parallel** - *Efim Kinber, Carl H. Smith, Mahendran Velauthapillai and Rolf Wiehagen* |

| | | **Language learning from texts: mind changes, limited memory and monotonicity** - *Efim Kinber and Frank Stephan* |

| | | **Fast and efficient reinforcement learning with truncated temporal differences** - *Pawel Cichosz and Jan J. Mulawka* |

| | | **Horizontal generalization** - *David H. Wolpert* |

| | | **More theorems about scale-sensitive dimensions and learning** - *Peter L. Bartlett and Philip M. Long* |

| | | **Sequential PAC learning** - *Dale Schuurmans and Russell Greiner* |

| | | **A preliminary PAC analysis of theory revision** - *Raymond J. Mooney* |

| | | **Bounds for Predictive Errors in the Statistical Mechanics of in Supervised Learning** - *Manfred Opper and David Haussler* |

| | | **Learning in Case-Based Classification Algorithms** - *Christoph Globig and Stefan Wess* |

| | | **Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons** - *Aleksandar Milosavljevic* |

| | | **DEXTER: A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration** - *D. M. Cohen, C. Kulikowski and H. Berman* |

| | | **A case study of explanation-based control** - *Gerald DeJong* |

| | | **A Branch and Bound Incremental Conceptual Clusterer** - *Arthur J. Nevins* |

| | | **On the Stochastic Complexity of Learning Realizable and Unrealizable Rules** - *Ronny Meir and Neri Merhav* |

| | | **Automated Refinement of First-Order Horn-Clause Domain Theories** - *Bradley L. Richards and Raymond J. Mooney* |

| | | **Online learning via congregational gradient descent** - *Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels and William A. Sethares* |

| | | **Use of Adaptive Networks to Define Highly Predictable Protein Secondary-Structure Classes** - *Alan S. Lapedes, Evan W. Steeg and Robert M. Farber* |

| | | **On the complexity of training neural networks with continuous activation functions** - *B. DasGupta, H. T. Siegelmann and E. Sontag* |

| | | **A game of prediction with expert advice** - *V. G. Vovk* |

| | | **NewsWeeder: learning to filter netnews** - *Ken Lang* |

| | | **Reducing the number of queries in self-directed learning** - *Yiqun L. Yin* |

| | | **Active exploration and learning in real-valued spaces using multi-armed bandit allocation indices** - *Marcos Salganicoff and Lyle H. Ungar* |

| | | **Exact learning of linear combinations of monotone terms from function value queries** - *Atsuyoshi Nakamura and Naoki Abe* |

| | | **On the Impact of Forgetting on Learning Machines** - *R. Freivalds, E. Kinber and C. Smith* |

| | | **On the complexity of teaching** - *Sally A. Goldman and Michael J. Kearns* |

| | | **Stable function approximation in dynamic programming** - *Geoffrey J. Gordon* |

| | | **Learning behaviors of automata from shortest counterexamples** - *F. Bergadano and S. Varricchio* |

| | | **Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets** - *Eduardo D. Sontag* |

| | | **On the Fourier spectrum of monotone functions** - *Nader Bshouty and Christino Tamon* |

| | | **A generalization of Sauer's Lemma** - *D. Haussler and P. Long* |

| | | **On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata** - *Takashi Yokomori* |

| | | **Miminum description length estimators under the optimal coding scheme** - *V. G. Vovk* |

| | | **Learning using group representations** - *Dan Boneh* |

| | | **A Loss Bound Model for On-line Stochastic Prediction Algorithms** - *Kenji Yamanishi* |

| | | **Some theorems concerning the free energy of (un)constrained stochastic Hopfield neural networks** - *Jan van den Berg and Jan C. Bioch* |

| | | **Characterizations of monotonic and dual monotonic language learning** - *T. Zeugmann, S. Lange and S. Kapur* |

| | | **Towards a mathematical theory of machine discovery from facts** - *Yasuhito Mukouchi and Setsuo Arikawa* |

| | | **Technical Note: Bias and the Quantification of Stability** - *Peter Turney* |

| | | **Automatic parameter selection by minimizing estimated error** - *Ron Kohavi and George H. John* |

| | | **Language learning without overgeneralization** - *S. Kapur and G. Bilardi* |

| | | **On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions** - *Akito Sakurai* |

| | | **A quantitative study of hypothesis selection** - *Philip W. L. Fong* |

| | | **Two Variations of Inductive Inference of Languages from Positive Data** - *Takashi Tabe and Thomas Zeugmann* |

| | | **On self-directed learning** - *Shai Ben-David, Nadav Eiron and Eyal Kushilevitz* |

| | | **Pac-Learning Recursive Logic Programs: Negative Results** - *William W. Cohen* |

| | | **On-line learning of linear functions** - *N. Littlestone, P. M. Long and M. K. Warmuth* |

| | | **Free to choose: investigating the sample complexity of active learning of real valued functions** - *Partha Niyogi* |

| | | **Topological Considerations in Composing Teams of Learning Machines** - *Kalvis Aps\=ıtis* |

| | | **Refutable inference of functions computed by loop programs** - *T. Miyahara* |

| | | **(Research Note) Classification accuracy: Machine learning vs. explicit knowledge acquisition** - *Arie Ben-David and Janice Mandel* |

| | | **Convergence results for the EM approach to Mixtures of Experts Architectures** - *M. I. Jordan and L. Xu* |

| | | **Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition** - *Cathy Wu, Michael Berry and Sailaja Shivakumar* |

| | | **Inferring reduced ordered decision graphs of minimum decision length** - *Arlindo L. Oliveira and Alberto Sangiovanni-Vincentelli* |

| | | **Solving Multiclass Learning Problems via Error-Correcting Output Codes** - *T. G. Dietterich and G. Bakiri* |

| | | **Language learning with some negative information** - *Ganesh Baliga, John Case and Sanjay Jain* |

| | | **Learning Fallible Deterministic Finite Automata** - *Dana Ron and Ronitt Rubinfeld* |

| | | **A Reply to Towell's Book Review of Neural Network Perception for Mobile Robot Guidance** - *Dean A. Pomerleau* |

| | | **On-line learning of binary and n-ary relations over multi-dimensional clusters** - *Atsuyoshi Nakamura and Naoki Abe* |

| | | **Recursion theoretic models of learning: some results and intuitions** - *Carl H. Smith and William I. Gasarch* |

| | | **Instance-based utile distinctions for reinforcement learning with hidden state** - *R. Andrew McCallum* |

| | | **Searching for Representations to Improve Protein Sequence Fold-Class Prediction** - *Thomas R. Ioerger, Larry A. Rendell and Shankar Subramaniam* |

| | | **Efficient memory-based dynamic programming** - *Jing Peng* |

| | | **Being taught can be faster than asking questions** - *Ronald L. Rivest and Yiqun L. Yin* |

| | | **On The Learnability Of Disjunctive Normal Form Formulas** - *Howard Aizenstein and Leonard Pitt* |

| | | **Sample compression, learnability, and the Vapnik-Chervonenkis dimension** - *Sally Floyd and Manfred Warmuth* |

| | | **Inductive Constraint Logic** - *Luc De Raedt and Wim Van Laer* |

| | | **Using multidimensional projection to find relations** - *Eduardo Pérez and Larry A. Rendell* |

| | | **Automatic selection of split criterion during tree growing based on node location** - *Carla E. Brodley* |

| | | **On the inductive inference of real valued functions** - *Kalvis Aps\=ıtis, Rīsiņš Freivalds and Carl H. Smith* |

| | | **Analogical logic program synthesis algorithm that can refute inappropriate similarities** - *Ken Sadohara and Makoto Haraguchi* |

| | | **Complexity of computing Vapnik-Chervonenkis dimension and some generalized dimensions** - *Ayumi Shinohara* |

| | | **Evaluation and selection of biases in machine learning** - *Diana F. Gordon and Marie desJardins* |

| | | **Protein folding: symbolic refinement competes with neural networks** - *Susan Craw and Paul Hutton* |

| | | **A note on the use of probabilities by mechanical learners** - *Eric Martin and Daniel Osherson* |

| | | **On learning bounded-width branching programs** - *Funda Ergün, Ravi S. Kumar and Ronitt Rubinfeld* |

| | | **Prudence in Vacillatory Language Identification** - *S. Jain and A. Sharma* |

| | | **Machine discovery of protein motifs** - *Darrell Conklin* |

| | | **Text categorization and relational learning** - *William W. Cohen* |

| | | **Concept learning with geometric hypotheses** - *David P. Dobkin and Dimitrios Gunopulos* |

| | | **Error detecting in inductive inference** - *R. Freivalds, E. B. Kinber and R. Wiehagen* |

| | | **Symbiosis in multimodal concept learning** - *Jukka Hekanaho* |

| | | **Inductive Inference of Formal Languages** - *Masako Sato* |

| | | **Learning with unreliable boundary queries** - *Avrim Blum, Prasad Chalasani, Sally A. Goldman and Donna K. Slonim* |

| | | **Rationality** - *Leslie G. Valiant* |

| | | **Refined Incremental Learning** - *S. Lange and T. Zeugmann* |

| | | **On the Intrinsic Complexity of Learning** - *Rīsiņš Freivalds, Efim Kinber and Carl H. Smith* |

| | | **Empirical support for Winnow and weighted-majority based algorithms: results on a calendar scheduling domain** - *Avrim Blum* |

| | | **Feature Construction during Tree Learning** - *G. Mehlsam, H. Kaindl and W. Barth* |

| | | **Declarative Bias for Specific-to-General ILP Systems** - *Hilde Adé, Luc De Raedt and Maurice Bruynooghe* |

| | | **K*: an instance-based learner using an entropic distance measure** - *John G. Cleary and Leonard E. Trigg* |

| | | **T-.1em.7exL-.31emP-.1em.4exS - a Term Rewriting Laboratory (not only) for Experiments in Automatic Program Synthesis** - *Gunter Grieser* |

| | | **Characterizing PAC-Learnability of Semilinear Sets** - *Naoki Abe* |

| | | **On handling tree-structured attributes in decision tree learning** - *Hussein Almuallim, Yasuhiro Akiba and Shigeo Kaneda* |

| | | **Comprehension Grammars Generated from Machine Learning of Natural Languages** - *Patrick Suppes, Michael Böttner and Lin Liang* |

| | | **Markov decision processes in large state spaces** - *Lawrence K. Saul and Satinder P. Singh* |

| | | **The Complexity of Learning Minor Closed Graph Classes** - *Carlos Domingo and John Shawe-Taylor* |

| | | **On the Sample Complexity of Weak Learning** - *S. A. Goldman, M. J. Kearns and R. E. Schapire* |

| | | **MDL learning of unions of simple pattern languages from positive examples** - *Pekka Kilpeläinen, Heikki Mannila and Esko Ukkonen* |

| | | **Cryptographic lower bounds for learnability of Boolean functions on the uniform distribution** - *Michael Kharitonov* |

| | | **Learning hierarchies from ambiguous natural language data** - *Takefumi Yamazaki, Michael J. Pazzani and Christopher Merz* |

| | | **General bounds on the mutual information between a parameter and n conditionally independent observations** - *David Haussler and Manfred Opper* |

| | | **A method for constructive learning of recurrent neural networks** - *Dong Chen, C. Lee Giles, Gordon Sun, Mark W. Goudreau, Hsing-Hen Chen and Yee-Chun Lee* |

| | | **An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts** - *Jianping Zhang and Ryszard S. Michalski* |

| | | **A lexically based semantic bias for theory revision** - *Clifford Brunk and Michael Pazzani* |

| | | **MDL and categorical theories (continued)** - *J. R. Quinlan* |

| | | **Genetic Algorithms, Operators, and DNA Fragment Assembly** - *Rebecca J. Parsons, Stephanie Forrest and Christian Burks* |

| | | **The power of procrastination in inductive inference: how it depends on used ordinal notations** - *Andris Ambainis* |

| | | **Towards Reduction Arguments for FINite Learning** - *Robert Daley and Bala Kalyanasundaram* |

| | | **The challenge of revising an impure theory** - *Russell Greiner* |

| | | **Learning in the presence of finitely or infinitely many irrelevant attributes** - *Avrim Blum, Lisa Hellerstein and Nick Littlestone* |

| | | **Learning Bayesian networks: the combination of knowledge and statistical data** - *David Heckerman, Dan Geiger and David M. Chickering* |

| | | **On the complexity of function learning** - *Peter Auer, Philip M. Long, W. Maass and Gerhard J. Woeginger* |

| | | **Introduction** - *Jude Shavlik, Lawrence Hunter and David Searls* |

| | | **A comparison of inductive algorithms for selective and non-selective Bayesian classifiers** - *Moninder Singh and Gregory M. Provan* |

| | | **Learning binary relations using weighted majority voting** - *Sally A. Goldman and Manfred K. Warmuth* |

| | | **Typed pattern languages and their learnability** - *Takeshi Koshiba* |

| | | **A Markovian Extension of Valiant's Learning Model** - *D. Aldous and U. Vazirani* |

| | | **Pattern Inference** - *T. Shinohara and S. Arikawa* |

| | | **Structuring Neural Networks and PAC Learning** - *E. Pippig* |

| | | **Average case analysis of a learning algorithm for ***mu*-DNF expressions - *Mostefa Golea* |

| | | **Regression NSS: an alternative to cross validation** - *Michael P. Perrone and Brian S. Blais* |

| | | **Not-so-nearly-minimal-size program inference** - *John Case, Mandayam Suraj and Sanjay Jain* |

| | | **Learning prototypical concept descriptions** - *Piew Datta and Dennis Kibler* |

| | | **Can PAC Learning Algorithms Tolerate Random Attribute Noise?** - *S. A. Goldman and R. H. Sloan* |

| | | **Tight worst-case loss bounds for predicting with expert advice** - *David Haussler, Jyrki Kivinen and Manfred K. Warmuth* |

| | | **A space-bounded learning algorithm for axis-parallel rectangles** - *Foued Ameur* |

| | | **Learning finite automata using local distinguishing experiments** - *Wei-Min Shen* |

| | | **Learning with probabilistic supervision** - *Padhraic Smyth* |

| | | **On efficient agnostic learning of linear combinations of basis functions** - *Wee Sun Lee, Peter L. Bartlett and Robert C. Williamson* |

| | | **The Complexity of Theory Revision** - *Russell Greiner* |

| | | **Case-based acquisition of place knowledge** - *Pat Langley and Karl Pfleger* |

| | | **Lessons from theory revision applied to constructive induction** - *Stephen K. Donoho and Larry A. Rendell* |

| | | **Reinforcement learning by stochastic hill climbing on discounted reward** - *Hajime Kimura, Masayuki Yamamura and Shigenobu Kobayashi* |

| | | **On the sample complexity of PAC learning half-spaces against the uniform distribution** - *Philip M. Long* |

| | | **Reductions for learning via queries** - *William Gasarch and Geoffrey R. Hird* |

| | | **The Structure of Intrinsic Complexity of Learning** - *Sanjay Jain and Arun Sharma* |

| | | **Gambling in a rigged casino: the adversarial multi-armed bandit problem** - *Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund and Robert E. Schapire* |

| | | **On the learnability and usage of acyclic probabilistic finite automata** - *Dana Ron, Yoram Singer and Naftali Tishby* |

| | | **Using heuristic search to expand knowledge-based neural networks** - *David W. Opitz and Jude W. Shavlik* |

| | | **Reflecting and Self-Confident Inductive Inference Machines** - *Klaus P. Jantke* |

| | | **A comparison of ID3 and backpropogation for English text-to-speech mapping** - *Thomas G. Dietterich, Hermann Hild and Ghulum Bakiri* |

| January | | **The EM algorithm and Information geometry in neural network learning** - *S. Amari* |

| September | | **Boosting a Weak Learning Algorithm by Majority** - *Y. Freund* |

| October | | **Algorithmic Learning Theory, 6th International Workshop, ALT '95, Fukuoka, Japan, October 18-20, 1995, Proceedings** - *Klaus P. Jantke and Takeshi Shinohara and Thomas Zeugmann* |