| 1988 | |  | Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises - J. L. McClelland and D. E. Rumelhart |
| | | Mathematical/Mechanical? Learners pay a price for Bayesianism - D. N. Osherson, M. Stob and S. Weinstein |
| | | Saving the Phenomenon: Requirements that Inductive Machines not Contradict Known Data - M. Fulk |
| | | Learning when Irrelevant Attributes Abound: A New Linear-threshold Algorithm - N. Littlestone |
| | | Efficient unsupervised learning - P. D. Laird |
| | | Functionality in Neural Nets at AAAI - L. Valiant |
| | | A Review of Machine Learning at AAAI-87 - Russell Greiner et al. |
| | | Quantifying Inductive Bias: AI Learning Algorithms and Valiant’s Learning Framework - D. Haussler |
| | | Learning theories in a subset of a polyadic logic - R. B. Banerji |
| | | Learning in neural networks - S. Judd |
| | | Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains - Robert J. Hall |
| | | On the learnability of finite automata - M. Li and U. Vazirani |
| | | Learning by Making Models - P. Laird |
| | | Strategies for Teaching Layered Networks Classification Tasks - B. S. Wittner and J. S. Denker |
| | | Criteria for Polynomial-Time Conceptual Clustering - Leonard Pitt and Robert E. Reinke |
| | | Learning from Good and Bad Data - Philip D. Laird |
| | | Learning pattern languages from a single initial example and from queries - A. Marron |
| | | Space Efficient Learning Algorithms - D. Haussler |
| | | Classifier Systems that Learn Internal World Models - Lashon B. Booker |
| | | Learning from noisy examples - D. Angluin and P. Laird |
| | | Computational limitations on learning from examples - L. Pitt and L. Valiant |
| | | Sparse Distributed Memory - P. Kanerva |
| | | Learning regular languages from counterexamples - O. H. Ibarra and T. Jiang |
| | | New Theoretical Directions in Machine Learning - D. Haussler |
| | | Learning probabilistic prediction functions - A. DeSantis, G. Markowsky and M. N. Wegman |
| | | Criteria for Polynomial Time Conceptual Clustering - L. Pitt and R. E. Reinke |
| | | Identifying languages from stochastic examples - D. Angluin |
| | | Learnability by fixed distributions - G. M. Benedek and A. Itai |
| | | Genetic Algorithms in Noisy Environments - J. Michael Fitzpatrick and John J. Grefenstette |
| | | A Tale of Two Classifier Systems - George G. Robertson and Rick L. Riolo |
| | | Supervised Learning of Probability Distributions by Neural Networks - E. Baum and F. Wilczek |
| | | Learning with hints - D. Angluin |
| | | Learning in threshold networks - P. Raghavan |
| | | News and Notes first of 88 - T. G. Dietterich |
| | | Requests for hints that return no hints - D. Angluin |
| | | Transformation of probabilistic learning strategies into deterministic learning strategies - R. Daley |
| | | Non-learnable classes of Boolean formulae that are closed under variable permutation - H. Shvaytser |
| | | Prudence in Language Learning - S. A. Kurtz and J. S. Royer |
| | | Learning k-DNF with Noise in the Attributes - G. Shackelford and D. Volper |
| | | Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets - R. P. Gorman and T. J. Sejnowski |
| | | Probability and Plurality for Aggregations of Learning Machines - L. Pitt and C. Smith |
| | | Learning Boolean Formulae or Finite Automata is as Hard as Factoring - M. Kearns and L. G. Valiant |
| | | Inductive inference: an abstract approach - J. C. Cherniavsky, M. Velauthapillai and R. Statman |
| | | Accelerated Learning in Layered Neural Networks - S. A. Solla, E. Levin and M. Fleisher |
| | | Learning to Predict by the Methods of Temporal Differences - Richard S. Sutton |
| | | Summary of the panel discussion - D. Angluin, L. Birnbaum, J. Feldman, R. Rivest and L. Valiant |
| | | Proc. 1st Annu. Workshop on Comput. Learning Theory - D. Haussler and L. Pitt |
| | | Learning and Programming in Classifier Systems - Richard K. Belew and Stephanie Forrest |
| | | Learning complicated concepts reliably and usefully - R. L. Rivest and R. Sloan |
| | | On Rationality and Learning - J. Doyle |
| | | A Learning Algorithm for Linear Operators - J. Mycielski |
| | | Functionality in neural networks - L. G. Valiant |
| | | Linear manifolds are learnable from positive examples - H. Shvaytser |
| | | Probabilistic Versus Deterministic Inductive Inference in Nonstandard Numberings - R. Freivalds, E. B. Kinber and R. Wiehagen |
| | | The power of vacillation - J. Case |
| | | Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms - John J. Grefenstette |
| | | Synthesising Inductive Expertise - D. Osherson, M. Stob and S. Weinstein |
| | | A Non-Iterative Maximum Entropy Algorithm - S. A. Goldman and R. L. Rivest |
| | | Genetic Algorithms and Machine Learning - D. E. Goldberg and J. H. Holland |
| | | Types of noise in data for concept learning - R. Sloan |
| | | Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm - N. Littlestone |
| | | Machine Learning as an Experimental Science - P. Langley |
| | | News and Notes second of 88 - T. G. Dietterich |
| | | Neurocomputing: Foundations of Research - J. A. Anderson and E. Rosenfeld |
| | | Some remarks about space-complexity of learning, and circuit complexity of recognizing - S. Boucheron and J. Sallantin |
| | | Scaling relationships in back-propagation learning - G. Tesauro and B. Janssens |
| | | Extending the Valiant learning model - J. Amsterdam |
| | | On the Power of Recursive Optimizers - T. Zeugmann |
| | | Inductive Syntactical Synthesis of Programs From Sample Computations - E. B. Kinber |
| | | Learning with Genetic Algorithms: An Overview - Kenneth De Jong |
| January | | The Valiant Learning Model: Extensions and Assessment - J. Amsterdam |
| March | | Machine Learning: the Human Connection - R. L. Rivest and W. Remmele |
| | | Exploiting Chaos to Predict the Future and Reduce Noise - J. D. Farmer and J. J. Sidorowich |
| | | A New Model for Inductive Inference - R. L. Rivest and R. Sloan |
| April | | Queries and Concept Learning - D. Angluin |
| May | | Diversity-Based Inference of Finite Automata - R. E. Schapire |
| | | Supervised learning and systems with excess degrees of freedom - M. I. Jordan |
| June | | Two New Frameworks for Learning - B. K. Natarajan and P. Tadepalli |
| July | | On the Learnability of DNF Formulae - L. Kucera, A. Marchetti-Spaccamela and M. Protasi |
| | | Automatic Pattern Recognition: A Study of the Probability of Error - L. Devroye |
| | | Nonuniform Learnability - G. M. Benedek and A. Itai |
| August | | Some Philosophical Problems with Formal Learning Theory - J. Amsterdam |
| | | A Pattern Classification Approach to Evaluation Function Learning - K. Lee and S. Mahajan |
| September | | A Study of Scaling and Generalization in Neural Networks - S. Ahmad |
| October | | Exemplar-based learning: theory and implementation - S. Salzberg |
| | | Special Issue on Genetic Algorithms - D. E. Goldberg and J. H. Holland |
| | | Learning a Probability Distribution Efficiently and Reliably - P. Laird and E. Gamble |
| November | | Equivalence Queries and DNF formulas - D. Angluin |
| December | | Thoughts on Hypothesis Boosting - M. Kearns |
