| 1987 | |  | Inferring parsers of context-free languages from structural examples - Y. Sakakibara |
| | | The Asymptotic Theory of Extreme Order Statistics - Janos Galambos |
| | | Simplifying Decision Trees - J. R. Quinlan |
| | | Learning one-counter languages in polynomial time - P. Berman and R. Roos |
| | | Research Papers in Machine Learning - P. Langley |
| | | Some Notes on Chernoff Bounds - R. H. Sloan |
| | | Testing and inductive inference: abstract approaches - J. Cherniavsky and R. Statman |
| | | Learning Functions from Examples - B. K. Natarajan |
| | | News and Notes first of 87 - T. G. Dietterich |
| | | Radial Basis Functions for Multivariable Interpolation: A Review - M. J. D. Powell |
| | | Debugging a DAG Efficiently - D. Angluin |
| | | Semi-supervised learning - L. Pitt and R. Board |
| | | Learning in permutation groups extended abstract - U. Feige and A. Shamir |
| | | Univresal sequential Coding of Single Messages - Y. M. Shtarkov |
| | | Theory Change via View Application in Instructionless Learning - Jeff Shrager |
| | | A Mean Field Theory Learning Algorithm for Neural Networks - C. Peterson and J. R. Anderson |
| | | Inference of visible simple assignment automata with planned experiments - R. Rivest and R. Schapire |
| | | Diversity-based inference of finite automata - R. L. Rivest and R. E. Schapire |
| | | Classifier Systems and the Animat Problem - Stewart W. Wilson |
| | | Inductive inference of logic programs based on algebraic semantics - Y. Sakakibara |
| | | Learning Syntax by Automata Induction - Robert C. Berwick and Sam Pilato |
| | | Inferring Parsers of Context Free Languages from Structural Examples - Y. Sakakibara |
| | | Inductive Inference - D. Angluin and C. Smith |
| | | Scaling relationships in back-propagation learning: dependence on training set size - G. Tesauro |
| | | A Version Space Approach to Learning Context-free Grammars - Kurt Vanlehn and William Ball |
| | | Algorithms in Combinatorial Geometry - H. Edelsbrunner |
| | | Robust learning in polynomial time: possibilities and limitations - M. Kearns and M. Li |
| | | Machine Learning and Grammar Induction - P. Langley |
| | | Automatic learning, rule extraction and generalization - J. Denker, D. Schwartz, B. Wittner, S. Solla, R. Howard, L. Jackel and J. Hopfield |
| | | A Review of the Fourth International Workshop on Machine Learning - Rogers P. Hall et al. |
| | | Learning Decision Lists - Ronald L. Rivest |
| | | Experiments with Incremental Concept Formation: UNIMEM - Michael Lebowitz |
| | | Neural Darwinism - The Theory of Neuronal Group Selection - G. M. Edelman |
| | | Applying Valiant’s Learning Framework to AI Concept Learning Problems - D. Haussler |
| | | News and Notes - T. G. Dietterich |
| | | On the learnability of Boolean formulae - M. Kearns, M. Li, L. Pitt and L. Valiant |
| | | On Language and Connectionism: Analysis of a Parallel Distributed Processing Model of Language Acquisition - S. Pinker and A. Prince |
| | | Efficient algorithms with neural networks behavior - S. Omohundro |
| | | On learning Boolean functions - B. K. Natarajan |
| | | Shift of Bias for Inductive Concept Learning - P. Utgoff |
| | | On convergence to the truth and nothing but the truth - K. Kelly and C. Glymour |
| | | Application of a General Learning Algorithm to the Control of Robotic Manipulators - W. T. Miller, F. H. Glanz and L. G. Kraft |
| | | Epsilon-nets and Simplex Range Queries - D. Haussler and E. Welzl |
| | | News and Notes second of 87 - T. G. Dietterich |
| | | Phoneme Recognition Using Time-Delay Neural Networks - A. Waibel, T. Hanazawa, G. Ginton and K. Shikano |
| | | Machine Learning and Concept Formation - P. Langley |
| | | Knowledge Acquisition Via Incremental Conceptual Clustering - Douglas H. Fisher |
| January | | Learning One Subprocedure per Lesson - K. VanLehn |
| February | | Parallel Networks that Learn to Pronounce English Text - T. J. Sejnowski and C. R. Rosenberg |
| March | | Stability and Looping in Connectionist Models with Assymmetric Weights - S. Porat |
| April | | Occam’s Razor - A. Blumer, A. Ehrenfeucht, D. Haussler and M. K. Warmuth |
| | | An Introduction to Computing with Neural Nets - R. P. Lippmann |
| | | Minimum Information Estimation of Structure - G. W. Hart |
| May | | Review of Five Empirical Learning Systems Within a Proposed Schemata - M. Gams and N. Lavrac |
| | | Combining Cross-Validation and Search - C. J. C. H. Watkins |
| | | Induction in Noisy Domains - P. Clark and T. Niblett |
| | | Constructing Decision Trees in Noisy Domains - T. Niblett |
| | | Assistant 86: A Knowledge-Elicitation Tool for Sophisticated Users - B I. K. Cestnik and I. Bratko |
| June | | A New Approach to Unsupervised Learning in Deterministic Environments - R. L. Rivest and R. E. Schapire |
| | | Recent Results on Boolean Concept Learning - M. Kearns, M. Li, L. Pitt and L. Valiant |
| | | Learning in a Layered Network with many Fixed-Function Hidden Nodes - N. Littlestone |
| | | Bias, Version Spaces, and Valiant’s Learning Framework - D. Haussler |
| | | Learning by Experimentation - J. G. Carbonell and Y. Gil |
| July | | A Physiological Basis for a Theory of Synapse Modification - M. F. Bear, L. N. Cooper and F. F. Ebner |
| | | Language Learning by Automata Induction - R. C. Berwick |
| | | Complexity of Connectionist Learning with Various Node Functions - J. S. Judd |
| | | A Mechanism for Early Piagetian Learning - G. L. Drescher |
| August | | Learning k-term DNF Formulas using Queries and Counterexamples - D. Angluin |
| | | Learning k-Bounded Context-Free Grammars - D. Angluin |
| | | Identification of Pattern Languages from Examples and Queries - A. Marron and K. Ko |
| September | | Convergence Results in the Hopfield Model - J. Kómlos and R. Paturi |
| | | SOAR: An architecture for General Intelligence - J. E. Laird, A. Newell and P. S. Rosenbloom |
| November | | Learning Regular Sets from Queries and Counterexamples - D. Angluin |
| December | | Learning propositional Horn sentences with hints - D. Angluin |
| | | Polynomially Sized Boolean Circuits are not Learnable - D. Beaver |
