| 1989 | |  | Trade-Off Among Parameters Affecting Inductive Inference - R. Freivalds, C. H. Smith and M. Velauthapillai |
| | | A greedy method for learning -DNF functions under the uniforn distribution - G. Pagallo and D. Haussler |
| | | LT Revisited: Explanation-Based Learning and the Logic of Principia Mathematica - Paul O’Rorke |
| | | Identifying $-decision trees and $-formulas with constrained instance queries - T. Hancock |
| | | The equivalence and learning of probabilistic automata - W. Tzeng |
| | | Identification of unions of languages drawn from an identifiable class - K. Wright |
| | | Learning Decision Trees from Random Examples - A. Ehrenfeucht and D. Haussler |
| | | Efficient Specialization of Relational Concepts - Kurt Vanlehn |
| | | Generalizing the PAC model: sample size bounds from metric dimension-based uniform convergence results - D. Haussler |
| | | Polynomial learning of semilinear sets - N. Abe |
| | | Learning Conjunctive Concepts in Structural Domains - D. Haussler |
| | | Fast Learning in Networks of Locally-Tuned Processing Units - J. Moody and C. Darken |
| | | On learning from exercises - B. K. Natarajan |
| | | Reliable and useful learning - J. Kivinen |
| | | Inductive Inference From Good Examples - R. Freivalds, E. B. Kinber and R. Wiehagen |
| | | Constant depth circuits, Fourier transform, and learnability - N. Linial, Y. Mansour and N. Nisan |
| | | The CN2 Induction Algorithm - Peter Clark and Tim Niblett |
| | | Learning structure from data: a survey - J. Pearl and R. Dechter |
| | | A Reconfigurable Analog VLSI Neural Network Chip - H. P. Graf, S. Satyanarayana and Y. Tsividis |
| | | Recursion Theoretic Characterizations of Language Learning - S. Jain and A. Sharma |
| | | Efficient NC algorithms for set cover with applications to learning and geometry - B. Berger, J. Rompel and P. W. Shor |
| | | Erratum one - Authorless |
| | | Learning Arm Kinematics and Dynamics - C. G. Atkeson |
| | | Computational Learning Theory: New Models and Algorithms - R. H. Sloan |
| | | On Learning Sets and Functions - B. K. Natarajan |
| | | Learning Faster than Promised by the Vapnik-Chervonenkis Dimension - A. Blumer and N. Littlestone |
| | | When Will Machines Learn? - Douglas B. Lenat |
| | | Refined Query Inference - E. B. Kinber and T. Zeugmann |
| | | Stochastic Complexity in Statistical Inquiry - J. Rissanen |
| | | Can Machine Learning Offer Anything to Expert Systems? - Bruce G. Buchanan |
| | | Learnability and the Vapnik-Chervonenkis Dimension - A. Blumer, A. Ehrenfeucht, D. Haussler and M. K. Warmuth |
| | | Probably-Approximate Learning over Classes of Distributions - B. K. Natarajan |
| | | On the complexity of learning from counterexamples - W. Maass and G. Turán |
| | | Induction from the general to the more general - K. T. Kelly |
| | | Coping with uncertainty in map learning - K. Basye, T. Dean and J. Vitter |
| | | Neural networks, principle components, and subspaces - E. Oja |
| | | Consistent inference of probabilities in layered networks: predictions and generalizations - N. Tishby, E. Levin and S. Solla |
| | | On the role of search for learning - S. A. Kurtz and C. H. Smith |
| | | The Vapnik-Chervonenkis Dimension: Information verses Complexity in Learning - Y. S. Abu-Mostafa |
| | | On the error probabilty of boolean concept descriptions - F. Bergadano and L. Saitta |
| | | Toward a Unified Science of Machine Learning - P. Langley |
| | | Equivalence queries and approximate fingerprints - D. Angluin |
| | | Approximation by Superpositions of a Sigmoidal Function - G. Cybenko |
| | | Elementary formal system as a unifying framework for language learning - S. Arikawa, T. Shinohara and A. Yamamoto |
| | | A polynomial-time algorithm for learning k-variable pattern languages from examples - M. Kearns and L. Pitt |
| | | The World Would Be a Better Place if Non-Programmers Could Program - John McDermott |
| | | On Metric Entripy, Vapnik-Chervonenkis Dimension, and Learnability for a Class of Distributions - S. Kulkarni |
| | | Representation Propoerties of Networks: Kolmogorov’s Theorm Is Irrelevant - T. Poggio and F. Girosi |
| | | Fast Learning in Multi-Resolution Hierarchies - J. Moody |
| | | Semi-Supervised Learning - R. A. Board and L. Pitt |
| | | Towards representation independence in PAC learning - M. Warmuth |
| | | An Empirical Comparison of Selection Measures for Decision-Tree Induction - John Mingers |
| | | The light bulb problem - R. Paturi, S. Rajasekaran and J. Reif |
| | | Learning nested differences of intersection-closed concept classes - D. Helmbold, R. Sloan and M. K. Warmuth |
| | | On Characterizing and Learning Some Classes of Read-once Functions - L. Hellerstein |
| | | A Critique of the Valiant Model - W. Buntine |
| | | From on-line to batch learning - N. Littlestone |
| | | Planning and learning in permutation groups - A. Fiat, S. Moses, A. Shamir, I. Shimshoni and G. Tardos |
| | | An Empirical Comparison of Pruning Methods for Decision Tree Induction - John Mingers |
| | | Knowledge of an Upper Bound on Grammar Size Helps Language Learning - S. Jain and A. Sharma |
| | | Training a 3-node neural net is NP-Complete - A. Blum and R. L. Rivest |
| | | Polynomial Learnability as a Formal Model of Natural Language Acquisition - Naoki Abe |
| | | Task-Structures, Knowledge Acquisition and Learning - B. Chandrasekaran |
| | | Training sequences - D. Angluin, W. Gasarch and C. Smith |
| | | Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-of-the-Art - John H. Boose and Brian R. Gaines |
| | | Conceptual Clustering, Categorization, and Polymorphy - Stephen José Hanson and Malcolm Bauer |
| | | Incremental Induction of Decision Trees - Paul E. Utgoff |
| | | Supporting Start-to-Finish Development of Knowledge Bases - Ray Bareiss, Bruce W. Porter and Kenneth S. Murray |
| | | A parametrization scheme for classifying models of learnability - S. Ben-David, G. M. Benedek and Y. Mansour |
| | | Automated Support for Building and Extending Expert Models - Mark A. Musen |
| | | Learning simple deterministic languages - H. Ishizaka |
| | | Automated Knowledge Acquisition for Strategic Knowledge - Thomas R. Gruber |
| | | Optimal unsupervised learning in a single-layer linear feedforward neural network - T. D. Sanger |
| | | Performance of a Stochastic Learning Chip - J. Alspector and R. B. Allen |
| | | An application of minimum description length principle to online r ecognition of handprinted alphanumerals - Q. Gao and M. Li |
| | | Space-bounded learning and the Vapnik-Chervonenkis dimension - S. Floyd |
| | | Editorial one - Jaime G. Carbonell |
| | | Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space - G. E. Hinton |
| | | Identifying decision trees with equivalence queries - T. Hancock |
| | | Learnability and Linguistic Theory - R. J. Matthews and W. Demopoulos |
| | | A Heuristic Approach to the Discovery of Macro-operators - Glenn A. Iba |
| | | A general lower bound on the number of examples needed for learning - A. Ehrenfeucht, D. Haussler, M. Kearns and L. G. Valiant |
| | | The Knowledge Level Reinterpreted: Modeling How Systems Interact - William J. Clancey |
| | | Bounding sample size with the Vapnik-Chervonenkis dimension - J. Shawe-Taylor, M. Anthony and R. L. Biggs |
| | | Learning read-once formulas using membership queries - L. Hellerstein and M. Karpinski |
| | | Monte-Carlo Inference and its Relations to Reliable Frequency Identification - E. B. Kinber and T. Zeugmann |
| | | Cryptographic limitations on learning Boolean formulae and finite automata - M. Kearns and L. G. Valiant |
| | | Mistake Bounds and Logarithmic Linear-threshold Learning Algorithms - N. Littlestone |
| | | What Size Net Gives Valid Generalization? - E. Baum and D. Haussler |
| | | Learning Automata - An Introduction - K. S. Narendra and M. A. L. Thathachar |
| | | Complexity issues in learning by neural nets - J. Lin and J. S. Vitter |
| | | A Statistical Approach to Learning and Generalization in Neural Networks - E. Levin, N. Tishby and S. Solla |
| | | Synthetic Neural Modelling: Comparisons of Population and Connectionist Approaches - Jr G. N. Reeke, O. Sporns and G. M. Edelman |
| | | A Study of Explanation-Based Methods for Inductive Learning - Nicholas S. Flann and Thomas G. Dietterich |
| | | Some Results on Learning - B. K. Natarajan |
| | | Proc. 2nd Annu. Workshop on Comput. Learning Theory - R. Rivest and D. Haussler and M. K. Warmuth |
| | | Probabilistic Inductive Inference - L. Pitt |
| | | Using queries to identify -formulas - D. Angluin |
| | | Inductive inference with bounded number of mind changes - M. Velauthapillai |
| | | Regressiveness - M. Fulk |
| | | Adaptive Neural Networks Using MOS Charge Storage - D. B. Schwartz, R. E. Howard and W. E. Hubbard |
| | | Synergy of clustering multiple backpropagation networks - N. Lincoln and J. Skrzypek |
| | | Informed parsimonious inference of prototypical genetic sequences - A. Milosavljevi’c, D. Haussler and J. Jurka |
| | | Genetic Algorithms in Search, Optimization, and Machine Learning - D. E. Goldberg |
| | | News and Notes first of 89 - Thomas G. Dietterich |
| | | News and Notes second of 89 - T. G. Dietterich |
| | | A Parallel Network that Learns to Play Backgammon - G. Tesauro and T. J. Sejnowski |
| | | A theory of learning simple concepts under simple distributions and average case complexity for the universal distribution - M. Li and P. M. B. Vitanyi |
| | | On approximate truth - D. N. Osherson, M. Stob and S. Weinstein |
| | | Editorial two - Jaime G. Carbonell |
| | | Convergence to nearly minimal size grammars by vacillating learning machines - S. Jain, A. Sharma and J. Case |
| February | | Approximation of Boolean functions by sigmoidal networks: Part I: XOR and other two-variable functions - E. K. Blum |
| | | Backpropagation Can Give Rise to Spurious Local Minima Even for Networks without Hidden Layers - E. D. Sontag and H. J. Sussmann |
| March | | Inferring Decision Trees Using the Minimum Description Length Principle - J. R. Quinlan and R. L. Rivest |
| May | | The Use of Artificial Neural Networks for Phonetic Recognition - H. C. Leung |
| | | Learning from Delayed Rewards - C. J. C. H. Watkins |
| | | The Computational Complexity of Machine Learning - M. Kearns |
| | | On the Computational Complexity of Training Simple Neural Networks - A. Blum |
| | | Back Propagation Fails to Separate Where Perceptrons Succeed - M. L. Brady, R. Raghavan and J. Slawny |
| June | | Finding Natural Clusters Through Entropy Minimization - R. S. Wallace |
| | | Neural Network Learning: Effects of Network and Training Set Size - N. Perugini |
| | | Tensor Manipulation Networks: Connectionist and Symbolic Approaches to Comprehension, Learning, and Planning - C. P. Dolan |
| July | | A ‘Neural’ Network that Learns to Play Backgammon - G. Tesauro and T. J. Sejnowski |
| August | | Learning in the Presence of Inaccurate Information - M. A. Fulk and S. Jain |
| | | Inductive Principles of the Search for Empirical Dependences Methods Based on Weak Convergence of Probability Measures - V. N. Vapnik |
| | | Accelerated Backpropagation Learning: Two Optimization Methods - R. Battiti |
| September | | A General Lower Bound on the Number of Examples Needed for Learning - A. Ehrenfeucht and D. Haussler |
| | | Made-up Minds: A Constructivist Approach to Artificial Intelligence - G. L. Drescher |
| | | Generalizing the PAC Model for Neural Net and Other Learning Applications - D. Haussler |
| October | | An Experimental Comparison of Connectionist and Conventional Classification Systems on Natural Data - P. C. Woodland and S. G. Smyth |
| | | Networks and the Best Approximation Property - T. Poggio and F. Girosi |
| | | Inductive Inference, DFAs, and Computational Complexity - L. Pitt |
| | | Towards Representation Independence in PAC-learning - M. K. Warmuth |
| | | Inductive Inference from Theory Laden Data - K. T. Kelly and C. Glymour |
| November | | Discovering the Structure of a Reactive Environment by Exploration - M. C. Mozer and J. Bachrach |
| | | Learnability in the Presence of Classification Noise - Y. Sakakibara |
| December | | Space-bounded learning and the Vapnik-Chervonenkis Dimension Ph.D - S. Floyd |
