| 1995 | |  | MDL and categorical theories continued - J. R. Quinlan |
| | | Reductions for learning via queries - William Gasarch and Geoffrey R. Hird |
| | | Inductive learning of reactive action models - Scott Benson |
| | | Learning decision lists and trees with equivalence-queries - Hans Ulrich Simon |
| | | A note on the use of probabilities by mechanical learners - Eric Martin and Daniel Osherson |
| | | Learning unions of tree patterns using queries - Hiroki Arimura, Hiroki Ishizaka and Takeshi Shinohara |
| | | Fast effective rule induction - William W. Cohen |
| | | Characterizations of Learnability for Classes of {0,,n}-valued Functions - S. Ben-David, N. Cesa-Bianchi, D. Haussler and P. M. Long |
| | | Efficient algorithms for learning to play repeated games against computationally bounded adversaries - Yoav Freund, Michael Kearns, Yishay Mansour, Dana Ron and Ronitt Rubinfeld |
| | | Breaking the Probability 1/2 Barrier in FIN-type Learning - R. Daley, B. Kalyanasundaram and M. Velauthapillai |
| | | Concept learning with geometric hypotheses - David P. Dobkin and Dimitrios Gunopulos |
| | | More or less efficient agnostic learning of convex polygons - Paul Fischer |
| | | NewsWeeder: learning to filter netnews - Ken Lang |
| | | A note on VC-dimension and measure of sets of reals - Shai Ben-David and Leonid Gurvits |
| | | Active exploration and learning in real-valued spaces using multi-armed bandit allocation indices - Marcos Salganicoff and Lyle H. Ungar |
| | | Recursive automatic bias selection for classifier construction - Carla E. Brodley |
| | | On approximately identifying concept classes in the limit - Satoshi Kobayashi and Takashi Yokomori |
| | | Recursion theoretic models of learning: some results and intuitionsProblems - William Gasarch and Carl H. Smith |
| | | Reducing the number of queries in self-directed learning - Yiqun L. Yin |
| | | On Aggregating Teams of Learning Machines - S. Jain and A. Sharma |
| | | Learnability of Kolmogorov-easy circuit expressions via queries - José L. Balcázar, Harry Buhrman and Montserrat Hermo |
| | | Piecemeal learning of an unknown environment - Margrit Betke, Ronald L. Rivest and Mona Singh |
| | | Hellerstein, Lisa and Pillaipakkamnatt, Krishnan and Raghavan, Vijay and Wilkins, Dawn - How many queries are needed to learn? |
| | | Learning strongly deterministic even linear languages from positive examples - Takeshi Koshiba, Erkki Mäkinen and Yuji Takada |
| | | Hill climbing beats genetic search on a Boolean circuit problem of Koza’s - Kevin J. Lang |
| | | On the optimal capacity of binary neural networks: rigorous combinatorial approaches - Jeong Han Kim and James R. Roche |
| | | On-line maximum likelihood prediction with respect to general loss functions - Kenji Yamanishi |
| | | Proper learning algorithm for functions of k terms under smooth distributions - Yoshifumi Sakai, Eiji Takimoto and Akira Maruoka |
| | | Guest Editor’s Introduction - Sally A. Goldman |
| | | Learning polynomials with queries: the highly noisy case - Oded Goldreich, Ronitt Rubinfeld and Madhu Sudan |
| | | DNF - if you can’t learn ’em, teach ’em: an interactive model of teaching - David H. Mathias |
| | | A comparative evaluation of voting and meta-learning on partitioned data - Philip K. Chan and Salvatore J. Stolfo |
| | | A lexically based semantic bias for theory revision - Clifford Brunk and Michael Pazzani |
| | | Probably Almost Discriminative Learning - Kenji Yamanishi |
| | | Comprehension Grammars Generated from Machine Learning of Natural Languages - Patrick Suppes, Michael Böttner and Lin Liang |
| | | Practical PAC Learning - Dale Schuurmans and Russell Greiner |
| | | MDL learning of unions of simple pattern languages from positive examples - Pekka Kilpeläinen, Heikki Mannila and Esko Ukkonen |
| | | Empirical support for Winnow and weighted-majority based algorithms: results on a calendar scheduling domain - Avrim Blum |
| | | Efficient memory-based dynamic programming - Jing Peng |
| | | Analogical logic program synthesis algorithm that can refute inappropriate similarities - Ken Sadohara and Makoto Haraguchi |
| | | Learning via Queries, Teams, and Anomalies - William Gasarch, Efim Kinber, Mark Pleszkoch, Carl Smith and Thomas Zeugmann |
| | | An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms - Dietrich Wettschereck and Thomas G. Dietterich |
| | | Function learning from interpolation - Martin Anthony and Peter Bartlett |
| | | Learning 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 to make rent-to-buy decisions with systems applications - P. Krishnan, Philip M. Long and Jeffrey Scott Vitter |
| | | Predictive Hebbian learning - Terrence J. Sejnowski, Peter Dayan and P. Read Montague |
| | | On genetic algorithms - Eric B. Baum, Dan Boneh and Charles Garrett |
| | | Learning of regular expressions by pattern matching - Alvis Brāzma |
| | | On The Learnability Of Disjunctive Normal Form Formulas - Howard Aizenstein and Leonard Pitt |
| | | Unsupervised learning of multiple motifs in biopolymers using expectation maximization - Timothy L. Bailey and Charles Elkan |
| | | Grammatical inference: an old and new paradigm - Yasubumi Sakakibara |
| | | Corrigendum for: Learnability of description logics - William W. Cohen and Haym Hirsh |
| | | A comparison of ID3 and backpropogation for English text-to-speech mapping - Thomas G. Dietterich, Hermann Hild and Ghulum Bakiri |
| | | Volume of Polyhedra - R. Seidel |
| | | Inductive inference of functions on the rationals - Douglas A. Cenzer and William R. Moser |
| | | Book Review: Neural Network Perception for Mobile Robot Guidance by Dean A. Pomerleau. Kluwer Academic Publishers, 1993. - Geoffrey Towell |
| | | Fast learning of k-term DNF formulas with queries - Avrim Blum and Stephen Rudich |
| | | Language learning with some negative information - Ganesh Baliga, John Case and Sanjay Jain |
| | | DEXTER: a system that experiments with choices of training data using expert knowledge in the domain of DNA hydration - Dawn M. Cohen, Casimir Kulikowski and Helen Berman |
| | | Learning from a population of hypotheses - Michael Kearns and H. Sebastian Seung |
| | | Theory and applications of agnostic PAC-learning with small decision trees - Peter Auer, Robert C. Holte and Wolfgang Maass |
| | | Being taught can be faster than asking questions - Ronald L. Rivest and Yiqun L. Yin |
| | | Retrofitting decision tree classifiers using kernel density estimation - Padhraic Smyth, Alexander Gray and Usama M. Fayyad |
| | | The challenge of revising an impure theory - Russell Greiner |
| | | Searching for Representations to Improve Protein Sequence Fold-Class Prediction - Thomas R. Ioerger, Larry A. Rendell and Shankar Subramaniam |
| | | Learning policies for partially observable environments: scaling up - Michael L. Littman, Anthony R. Cassandra and Leslie Pack Kaelbling |
| | | Introduction - Jude Shavlik, Lawrence Hunter and David Searls |
| | | Bounds for Predictive Errors in the Statistical Mechanics of in Supervised Learning - Manfred Opper and David Haussler |
| | | Two Variations of Inductive Inference of Languages from Positive Data - T. Tabe and T. Zeugmann |
| | | Error-correcting output coding corrects bias and variance - Eun Bae Kong and Thomas G. Dietterich |
| | | Criteria for specifying machine complexity in learning - Changfeng Wang and Santosh S. Venkatesh |
| | | Characterizations of monotonic and dual monotonic language learning - T. Zeugmann, S. Lange and S. Kapur |
| | | The Complexity of Theory Revision - Russell Greiner |
| | | Modeling Incremental Learning from Positive Data - S. Lange and T. Zeugmann |
| | | Machine discovery of protein motifs - Darrell Conklin |
| | | Use of Adaptive Networks to Define Highly Predictable Protein Secondary-Structure Classes - Alan S. Lapedes, Evan W. Steeg and Robert M. Farber |
| | | Learning using group representations - Dan Boneh |
| | | Inductive constraint logic - Luc De Raedt and Wim Van Laer |
| | | On the Impact of Forgetting on Learning Machines - R. Freivalds, E. Kinber and C. Smith |
| | | An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts - Jianping Zhang and Ryszard S. Michalski |
| | | Online learning versus offline learning - Shai Ben-David, Eyal Kushilevitz and Yishay Mansour |
| | | Learning ordered binary decision diagrams - Ricard Gavaldà and David Guijarro |
| | | Declarative Bias for Specific-to-General ILP Systems - Hilde Adé, Luc De Raedt and Maurice Bruynooghe |
| | | Learning with unreliable boundary queries - Avrim Blum, Prasad Chalasani, Sally A. Goldman and Donna K. Slonim |
| | | Shifting Vocabulary Bias in Speedup Learning - Devika Subramanian |
| | | Learning by observation and practice: an incremental approach for planning operator acquisition - Xuemei Wang |
| | | Monotonicity maintenance in information-theoretic machine learning algorithms - Arie Ben-David |
| | | Tracking the best expert - Mark Herbster and Manfred Warmuth |
| | | Learning by extended statistical queries and its relation to PAC learning - Eli Shamir and Clara Schwartzman |
| | | The Appropriateness of Predicate Invention as Bias Shift Operation in ILP - Irene Stahl |
| | | Average case analysis of a learning algorithm for -DNF expressions - Mostefa Golea |
| | | Automated Refinement of First-Order Horn-Clause Domain Theories - Bradley L. Richards and Raymond J. Mooney |
| | | 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 |
| | | Noisy inference and oracles - Frank Stephan |
| | | Simple learning algorithms using divide and conquer - Nader H. Bshouty |
| | | Gambling in a rigged casino: the adversarial multi-armed bandit problem - Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund and Robert E. Schapire |
| | | Is the pocket algorithm optimal? - Marco Muselli |
| | | On-line learning of binary lexical relations using two-dimensional weighted majority algorithms - Naoki Abe, Hang Li and Atsuyoshi Nakamura |
| | | On handling tree-structured attributes in decision tree learning - Hussein Almuallim, Yasuhiro Akiba and Shigeo Kaneda |
| | | Committee-based sampling for training probabilistic classifiers - Ido Dagan and Sean P. Engelson |
| | | Efficient learning with virtual threshold gates - Wolfgang Maass and Manfred K. Warmuth |
| | | Kolmogorov numberings and minimal identification - Rusins Freivalds and Sanjay Jain |
| | | Tracking the best disjunction - Peter Auer and Manfred Warmuth |
| | | A Bayesian analysis of algorithms for learning finite functions - James Cussens |
| | | Learning nested differences in the presence of malicious noise - Peter Auer |
| | | On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata - Yokomori Takashi |
| | | Finite Identification of Functions by Teams with Success Ratio 12 and Above - Sanjay Jain, Arun Sharma and Mahendran Velauthapillai |
| | | The power of procrastination in inductive inference: how it depends on used ordinal notations - Andris Ambainis |
| | | Learning and Consistency - R. Wiehagen and T. Zeugmann |
| | | Regression NSS: an alternative to cross validation - Michael P. Perrone and Brian S. Blais |
| | | Sample sizes for sigmoidal neural networks - John Shawe-Taylor |
| | | Refined Incremental Learning - S. Lange and T. Zeugmann |
| | | Application of Kolmogorov complexity to inductive inference with limited memory - Andris Ambainis |
| | | Learning with rare cases and small disjuncts - Gary M. Weiss |
| | | Language learning from membership queries and characteristic examples - Hiroshi Sakamoto |
| | | Learning Fallible Deterministic Finite Automata - Dana Ron and Ronitt Rubinfeld |
| | | Fast and efficient reinforcement learning with truncated temporal differences - Pawel Cichosz and Jan J. Mulawka |
| | | On the complexity of function learning - Peter Auer, Philip M. Long, Wolfgang Maass and Gerhard J. Woeginger |
| | | On Pruning and averaging decision trees - Jonathan J. Oliver and David J. Hand |
| | | Lessons from theory revision applied to constructive induction - Stephen K. Donoho and Larry A. Rendell |
| | | On the Fourier spectrum of monotone functions - Nader Bshouty and Christino Tamon |
| | | Learning orthogonal F-Horn formulas - Akira Miyashiro, Eiji Takimoto, Yoshifumi Sakai and Akira Maruoka |
| | | Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition - Cathy Wu, Michael Berry and Sailaja Shivakumar |
| | | Predicting nearly as well as the best pruning of a decision tree - D. P. Helmbold and R. E. Schapire |
| | | Visualizing high-dimensional structure with the incremental grid-growing neural network - Justine Blackmore and Risto Miikkulainen |
| | | Inferring a DNA sequence from erroneous copies abstract - John Kececioglu, Ming Li and John Tromp |
| | | From noise-free to noise-tolerant and from on-line to batch learning - Norbert Klasner and Hans Ulrich Simon |
| | | Complexity Issues for Vacillatory Function Identification - J. Case, S. Jain and A. Sharma |
| | | Miminum description length estimators under the optimal coding scheme - V. G. Vovk |
| | | Technical and scientific issues of KDD - Yves Kodratoff |
| | | Learning in the presence of finitely or infinitely many irrelevant attributes - Avrim Blum, Lisa Hellerstein and Nick Littlestone |
| | | On the learnability of Z_N-DNF formulas - Nader H. Bshouty, Zhixiang Chen, Scott E. Decatur and Steven Homer |
| | | On learning decision committees - Richard Nock and Olivier Gascuel |
| | | Case-based acquisition of place knowledge - Pat Langley and Karl Pfleger |
| | | Reflecting and self-confident inductive inference machines - Klaus P. Jantke |
| | | Worst-case quadratic loss bounds for on-line prediction of linear functions by gradient descent - N. Cesa-Bianchi, P. Long and M. K. Warmuth |
| | | ALECSYS and the AutonoMouse: learning to control a real robot by distributed classifier systems - Marco Dorigo |
| | | Trading Monotonicity Demands versus Mind Changes - S. Lange and T. Zeugmann |
| | | Discovering solutions with low Kolmogorov complexity and high generalization capability - Jürgen Schmidhuber |
| | | Learning to reason with a restricted view - Roni Khardon and Dan Roth |
| | | Sequential PAC learning - Dale Schuurmans and Russell Greiner |
| | | Learning DNF over the uniform distribution using a quantum example oracle - Nader H. Bshouty and Jeffrey C. Jackson |
| | | Bounding VC-dimension for neural networks: progress and prospects - Marek Karpinski and Angus Macintyre |
| | | Learning to model sequences generated by switching distributions - Yoav Freund and Dana Ron |
| | | Learning hierarchies from ambiguous natural language data - Takefumi Yamazaki, Michael J. Pazzani and Christopher Merz |
| | | Learning internal representations - Jonathan Baxter |
| | | Multivariate decision trees - Carla E. Brodley and Paul E. Utgoff |
| | | Bounding the Vapnik-Chervonenkis dimension of concept classes parametrized by real numbers - Paul W. Goldberg and Mark R. Jerrum |
| | | Learning by a population of perceptrons - Kukjin Kang and Jong-Hoon Oh |
| | | Specification and simulation of statistical query algorithms for efficiency and noise tolerance - Javed A. Aslam and Scott E. Decatur |
| | | Stable function approximation in dynamic programming - Geoffrey J. Gordon |
| | | Approximation and learning of convex superpositions - Leonid Gurvits and Pascal Koiran |
| | | Learning binary relations using weighted majority voting - Sally A. Goldman and Manfred K. Warmuth |
| | | Ant-Q:a reinforcement learning approach to the traveling salesman problem - Luca M. Gambardella and Marco Dorigo |
| | | Prudence in Vacillatory Language Identification - S. Jain and A. Sharma |
| | | Cognitive Computation Extended Abstract - Leslie G. Valiant |
| | | Learning from a mixture of labeled and unlabeled examples with parametric side information - Joel Ratsaby and Santosh S. Venkatesh |
| | | Lange and Wiehagen’s Pattern Language Learning Algorithm: An Average-Case Analysis with respect to its Total Learning Time - T. Zeugmann |
| | | Randomized approximate aggregating strategies and their applications to prediction and discrimination - Kenji Yamanishi |
| | | General bounds on the mutual information between a parameter and n conditionally independent observations - David Haussler and Manfred Opper |
| | | On polynomial-time learnability in the limit of strictly deterministic automata - Takashi Yokomori |
| | | Generalized teaching dimensions and the query complexity of learning - Tibor Hegedüs |
| | | Compression-based discretization of continuous attributes - Bernhard Pfahringer |
| | | The query complexity of learning some subclasses of context-free grammars - Carlos Domingo and Victor Lavín |
| | | Simple PAC learning of simple decision lists - Jorge Castro and José L. Balcázar |
| | | Online learning via congregational gradient descent - Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels and William A. Sethares |
| | | 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 |
| | | Efficient learning of real time one-counter automata - Amr F. Fahmy and Robert S. Roos |
| | | A Reply to Towell’s Book Review of Neural Network Perception for Mobile Robot Guidance - Dean A. Pomerleau |
| | | On self-directed learning - Shai Ben-David, Nadav Eiron and Eyal Kushilevitz |
| | | Neural networks for full-scale protein sequence classification: sequence encoding with singular value decomposition - Cathy Wu, Michael Berry, Sailaja Shivakumar and Jerry McLarty |
| | | Language learning from texts: mind changes, limited memory and monotonicity - Efim Kinber and Frank Stephan |
| | | Research Note Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition - Arie Ben-David and Janice Mandel |
| | | Inductive Policy: The Pragmatics of Bias Selection - John Foster Provost and Bruce G. Buchanan |
| | | Q-learning for bandit problems - Michael O. Duff |
| | | Text categorization and relational learning - William W. Cohen |
| | | When won’t membership queries help? - Dana Angluin and Michael Kharitonov |
| | | Learning via queries and oracles - Frank Stephan |
| | | On the computational power of neural nets - Hava T. Siegelmann and Eduardo D. Sontag |
| | | A comparison of inductive algorithms for selective and non-selective Bayesian classifiers - Moninder Singh and Gregory M. Provan |
| | | Symbiosis in multimodal concept learning - Jukka Hekanaho |
| | | Technical Note: Bias and the Quantification of Stability - Peter Turney |
| | | Piecemeal graph exploration by a mobile robot - Baruch Awerbuch, Margrit Betke, Ronald L. Rivest and Mona Singh |
| | | The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces - Andrew W. Moore and Christopher G. Atkeson |
| | | Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension - D. Haussler |
| | | Removing the genetics from the standard genetic algorithm - Shumeet Baluja and Rich Caruana |
| | | Polynomial bounds for VC dimension of sigmoidal neural networks - Marek Karpinski and Angus Macintyre |
| | | Some theorems concerning the free energy of un constrained stochastic Hopfield neural networks - Jan van den Berg and Jan C. Bioch |
| | | Encouraging Experimental Results on Learning CNF - Raymond J. Mooney |
| | | A quantitative study of hypothesis selection - Philip W. L. Fong |
| | | Four types of noise in data for PAC Learning - R. H. Sloan |
| | | Trading monotonicity demands versus efficiency - S. Lange and T. Zeugmann |
| | | Efficient algorithms for finding multi-way splits for decision trees - Truxton Fulton, Simon Kasif and Steven Salzberg |
| | | Tight worst-case loss bounds for predicting with expert advice - David Haussler, Jyrki Kivinen and Manfred K. Warmuth |
| | | Probabilistic language learning under monotonicity constraints - Léa Meyer |
| | | Efficient learning from delayed rewards through symbolic evolution - David E. Moriarty and Risto Miikkulainen |
| | | On the complexity of teaching - Sally A. Goldman and Michael J. Kearns |
| | | General Bounds for Predictive Errors in Supervised Learning - Manfred Opper and David Haussler |
| | | Supervised and unsupervised discretization of continuous features - James Dougherty, Ron Kohavi and Mehran Sahami |
| | | K*: an instance-based learner using an entropic distance measure - John G. Cleary and Leonard E. Trigg |
| | | Learning collection fusion strategies for information retrieval - Geoffrey Towell, Ellen M. Voorhees, Narendra K. Gupta and Ben Johnson-Laird |
| | | Comparing several linear-threshold learning algorithms on tasks involving superfluous attributes - Nick Littlestone |
| | | A Guided Tour Across the Boundaries of Learning Recursive Languages - T. Zeugmann and S. Lange |
| | | The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant - Jyrki Kivinen and Manfred K. Warmuth |
| | | On the Complexity of Function Learning - Peter Auer et al. |
| | | Learning sparse linear combinations of basis functions over a finite domain - Atsuyoshi Nakamura and Shinji Miura |
| | | A case study of explanation-based control - Gerald DeJong |
| | | Automatic parameter selection by minimizing estimated error - Ron Kohavi and George H. John |
| | | A branch and bound conceptual clusterer - Arthur J. Nevins |
| | | A decision-theoretic generalization of on-line learning and an application to boosting - Yoav Freund and Robert E. Schapire |
| | | Inferring reduced ordered decision graphs of minimum decision length - Arlindo L. Oliveira and Alberto Sangiovanni-Vincentelli |
| | | The complexity of learning minor closed graph classes - Carlos Domingo and John Shawe-Taylor |
| | | Complexity of network training for classes of neural networks - Charles C. Pinter |
| | | Using multidimensional projection to find relations - Eduardo Pérez and Larry A. Rendell |
| | | Increasing the performance and consistency of classification trees by using the accuracy criterion at the leaves - David J. Lubinsky |
| | | Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning - Thomas Dean et al. |
| | | Reinforcement learning by stochastic hill climbing on discounted reward - Hajime Kimura, Masayuki Yamamura and Shigenobu Kobayashi |
| | | TD models: modeling the world at a mixture of time scales - Richard S. Sutton |
| | | On learning decision trees with large output domains - Nader H. Bshouty, Christino Tamon and David K. Wilson |
| | | A space-bounded learning algorithm for axis-parallel rectangles - Foued Ameur |
| | | A generalization of Sauer’s Lemma - D. Haussler and P. Long |
| | | Support-vector networks - Corinna Cortes and Vladimir Vapnik |
| | | Instance-based utile distinctions for reinforcement learning with hidden state - R. Andrew McCallum |
| | | Automatic speaker recognition: an application of machine learning - Brett Squires and Claude Sammut |
| | | Characterizing rational versus exponential learning curves - Dale Schuurmans |
| | | Robust trainability of single neurons - Klaus-U. Höffgen, Hans-U. Simon and Kevin S. Van Horn |
| | | On the learnability and usage of acyclic probabilistic finite automata - Dana Ron, Yoram Singer and Naftali Tishby |
| | | Simple learning algorithms for decision trees and multivariate polynomials - Nader H. Bshouty and Yishay Mansour |
| | | Stochastic complexity in learning - J. Rissanen |
| | | Bounds on the classification error of the nearest neighbor rule - John A. Drakopoulos |
| | | Rationality - Leslie G. Valiant |
| | | Classifying recursive predicates and languages - R. Wiehagen, C. H. Smith and T. Zeugmann |
| | | More theorems about scale-sensitive dimensions and learning - Peter L. Bartlett and Philip M. Long |
| | | Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons - Aleksandar Milosavljevi\‘c |
| | | Incremental learning of logic programs - M. R. K. Krishna Rao |
| | | Can PAC Learning Algorithms Tolerate Random Attribute Noise? - S. A. Goldman and R. H. Sloan |
| | | Free to choose: investigating the sample complexity of active learning of real valued functions - Partha Niyogi |
| | | Typed pattern languages and their learnability - Takeshi Koshiba |
| | | On-line learning of binary and n-ary relations over multi-dimensional clusters - Atsuyoshi Nakamura and Naoki Abe |
| | | Noise-tolerant parallel learning of geometric concepts - Nader H. Bshouty, Sally A. Goldman and David H. Mathias |
| | | Markov decision processes in large state spaces - Lawrence K. Saul and Satinder P. Singh |
| | | Learning recursive functions from approximations - John Case, Susanne Kaufmann, Efim Kinber and Martin Kummer |
| | | Learning distributions by their density levels - a paradigm for learning without a teacher - Shai Ben-David and Michael Lindenbaum |
| | | On learning from noisy and incomplete examples - Scott E. Decatur and Rosario Gennaro |
| | | A game of prediction with expert advice - V. G. Vovk |
| | | Research Note: Classification accuracy: machine learning vs. explicit knowledge acquisition - Ben-David Arie and Janice Mande |
| | | Characterizations of learnability for classes of {0,...n}-valued functions - Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler and Philip M. Long |
| | | Additive versus exponentiated gradient updates for linear prediction - Jyrki Kivinen and Manfred K. Warmuth |
| | | On learning bounded-width branching programs - Funda Ergün, Ravi S. Kumar and Ronitt Rubinfeld |
| | | The discovery of algorithmic probability: a guide for the programming of true creativity - Ray J. Solomonoff |
| | | On-line learning of linear functions - N. Littlestone, P. M. Long and M. K. Warmuth |
| | | A note on learning multivariate polynomials under the uniform distribution - Nader H. Bshouty |
| | | Automatic selection of split criterion during tree growing based on node location - Carla E. Brodley |
| | | Learning Bayesian networks: the combination of knowledge and statistical data - David Heckerman, Dan Geiger and David M. Chickering |
| | | How to use expert advice in the case when actual values of estimated events remain unknown - Olga Mitina and Nikolai Vereshchagin |
| | | Explanation-based learning and reinforcement learning: a unified view - Thomas G. Dietterich and Nicholas S. Flann |
| | | Learning behaviors of automata from shortest counterexamples - F. Bergadano and S. Varricchio |
| | | On efficient agnostic learning of linear combinations of basis functions - Wee Sun Lee, Peter L. Bartlett and Robert C. Williamson |
| | | Mutual Information and Bayes Methods for Learning a Distribution - David Haussler and Manfred Opper |
| | | Genetic Algorithms, Operators, and DNA Fragment Assembly - Rebecca J. Parsons, Stephanie Forrest and Christian Burks |
| | | Horizontal generalization - David H. Wolpert |
| | | Evaluation and selection of biases in machine learning - Diana F. Gordon and Marie desJardins |
| | | Sample compression, learnability, and the Vapnik-Chervonenkis dimension - Sally Floyd and Manfred Warmuth |
| | | Learning proof heuristics by adapting parameters - Matthias Fuchs |
| | | An inductive learning approach to prognostic prediction - W. Nick Street, O. L. Mangasarian and W. H. Wolberg |
| | | On a Question about Learning Nearly Minimal Programs - S. Jain |
| | | A Branch and Bound Incremental Conceptual Clusterer - Arthur J. Nevins |
| | | Protein folding: symbolic refinement competes with neural networks - Susan Craw and Paul Hutton |
| | | Simulating teams with many conjectures - Bala Kalyanasundaram and Mahendran Velauthapillai |
| | | Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets - Eduardo D. Sontag |
| | | Machine induction without revolutionary paradigm shifts - John Case, Sanjay Jain and Arun Sharma |
| | | Learning prototypical concept descriptions - Piew Datta and Dennis Kibler |
| | | Residual algorithms: reinforcement learning with function approximation - Leemon Baird |
| | | On the inductive inference of real valued functions - Kalvis Apsītis, Rīsiņš Freivalds and Carl H. Smith |
| January | | The EM algorithm and Information geometry in neural network learning - S. Amari |
| March | | The Structure of Intrinsic Complexity of Learning - S. Jain and A. Sharma |
| June | | On Weak Learning - D. Helmbold and M. K. Warmuth |
| July | | A comparison of new and old algorithms for a mixture estimation problem - D. Helmbold, R. E. Schapire, Y. Singer and M. K. Warmuth |
| September | | Boosting a Weak Learning Algorithm by Majority - Y. Freund |
| October | | Algorithmic Learning Theory, 6th International Workshop, ALT’95, Fukuoka, Japan - K. P. Jantke and T. Shinohara and T. Zeugmann |
