1988 | | | **Summary of the panel discussion** - *D. Angluin, L. Birnbaum, J. Feldman, R. Rivest and L. Valiant* |

| | | **Supervised Learning of Probability Distributions by Neural Networks** - *E. Baum and F. Wilczek* |

| | | **Learning complicated concepts reliably and usefully** - *R. L. Rivest and R. Sloan* |

| | | **Functionality in Neural Nets (at AAAI)** - *L. Valiant* |

| | | **Learning probabilistic prediction functions** - *A. DeSantis, G. Markowsky and M. N. Wegman* |

| | | **Probabilistic Versus Deterministic Inductive Inference in Nonstandard Numberings** - *R. Freivalds, E. B. Kinber and R. Wiehagen* |

| | | **Accelerated Learning in Layered Neural Networks** - *S. A. Solla, E. Levin and M. Fleisher* |

| | | **Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains** - *Robert J. Hall* |

| | | **Learning with hints** - *D. Angluin* |

| | | **Criteria for Polynomial-Time (Conceptual) Clustering** - *Leonard Pitt and Robert E. Reinke* |

| | | **Linear manifolds are learnable from positive examples** - *H. Shvaytser* |

| | | **Sparse Distributed Memory** - *P. Kanerva* |

| | | **Learning by Making Models** - *P. Laird* |

| | | **Space Efficient Learning Algorithms** - *D. Haussler* |

| | | **Scaling relationships in back-propagation learning** - *G. Tesauro and B. Janssens* |

| | | **Functionality in neural networks** - *L. G. Valiant* |

| | | **Learning from noisy examples** - *D. Angluin and P. Laird* |

| | | **Machine Learning as an Experimental Science** - *P. Langley* |

| | | **A Tale of Two Classifier Systems** - *George G. Robertson and Rick L. Riolo* |

| | | **Identifying languages from stochastic examples** - *D. Angluin* |

| | | **A Learning Algorithm for Linear Operators** - *J. Mycielski* |

| | | **Learning in threshold networks** - *P. Raghavan* |

| | | **Classifier Systems that Learn Internal World Models** - *Lashon B. Booker* |

| | | **Learning from Good and Bad Data** - *Philip D. Laird* |

| | | **Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework** - *D. Haussler* |

| | | **Learning to Predict by the Methods of Temporal Differences** - *Richard S. Sutton* |

| | | **On the Learnability of DNF Formulae** - *L. Kucera, A. Marchetti-Spaccamela and M. Protasi* |

| | | **Strategies for Teaching Layered Networks Classification Tasks** - *B. S. Wittner and J. S. Denker* |

| | | **New Theoretical Directions in Machine Learning** - *D. Haussler* |

| | | **Non-learnable classes of Boolean formulae that are closed under variable permutation** - *H. Shvaytser* |

| | | **Learning regular languages from counterexamples** - *O. H. Ibarra and T. Jiang* |

| | | **Computational limitations on learning from examples** - *Leonard Pitt and Leslie G. Valiant* |

| | | **Learning with Genetic Algorithms: An Overview** - *Kenneth De Jong* |

| | | **Some remarks about space-complexity of learning, and circuit complexity of recognizing** - *S. Boucheron and J. Sallantin* |

| | | **Genetic Algorithms and Machine Learning** - *D. E. Goldberg and J. H. Holland* |

| | | **Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets** - *R. P. Gorman and T. J. Sejnowski* |

| | | **Prudence in Language Learning** - *S. A. Kurtz and J. S. Royer* |

| | | **Inductive Syntactical Synthesis of Programs From Sample Computations** - *E. B. Kinber* |

| | | **Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises** - *J. L. McClelland and D. E. Rumelhart* |

| | | **Inductive inference: an abstract approach** - *J. C. Cherniavsky, M. Velauthapillai and R. Statman* |

| | | **Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm** - *N. Littlestone* |

| | | **Learning pattern languages from a single initial example and from queries** - *A. Marron* |

| | | **Mathematical/Mechanical? Learners pay a price for Bayesianism** - *D. N. Osherson, M. Stob and S. Weinstein* |

| | | **On the Power of Recursive Optimizers** - *Thomas Zeugmann* |

| | | **Saving the Phenomenon: Requirements that Inductive Inference Machines not Contradict Known Data** - *M. A. Fulk* |

| | | **Learning Boolean Formulae or Finite Automata is as Hard as Factoring** - *M. Kearns and L. G. Valiant* |

| | | **Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms** - *John J. Grefenstette* |

| | | **Types of noise in data for concept learning** - *R. Sloan* |

| | | **Proceedings of the First Annual Workshop on Computational Learning Theory** - *D. Haussler and L. Pitt* |

| | | **Learning and Programming in Classifier Systems** - *Richard K. Belew and Stephanie Forrest* |

| | | **Grammatical inference for even linear languages based on control sets** - *Yuji Takada* |

| | | **A Non-Iterative Maximum Entropy Algorithm** - *S. A. Goldman and R. L. Rivest* |

| | | **Transformation of probabilistic learning strategies into deterministic learning strategies** - *R. Daley* |

| | | **Learnability by fixed distributions** - *G. M. Benedek and A. Itai* |

| | | **Requests for hints that return no hints** - *D. Angluin* |

| | | **Synthesising Inductive Expertise** - *Daniel N. Osherson, Michael Stob and Scott Weinstein* |

| | | **The power of vacillation** - *J. Case* |

| | | **Extending the Valiant learning model** - *J. Amsterdam* |

| | | **Learning theories in a subset of a polyadic logic** - *R. B. Banerji* |

| | | **Learning k-DNF with Noise in the Attributes** - *G. Shackelford and D. Volper* |

| | | **On the learnability of finite automata** - *M. Li and U. Vazirani* |

| | | **On Rationality and Learning** - *J. Doyle* |

| | | **Efficient unsupervised learning** - *P. D. Laird* |

| | | **Probability and Plurality for Aggregations of Learning Machines** - *L. Pitt and C. Smith* |

| | | **Genetic Algorithms in Noisy Environments** - *J. Michael Fitzpatrick and John J. Grefenstette* |

| | | **Learning in neural networks** - *S. Judd* |

| | | **Neurocomputing: Foundations of Research** - *J. A. Anderson and E. Rosenfeld* |

| | | **A Review of Machine Learning at AAAI-87** - *R. Greiner, B. Silver, S. Becker and M. GrĂ¼ninger* |

| January | | **The Valiant Learning Model: Extensions and Assessment** - *J. Amsterdam* |

| March | | **Machine Learning: the Human Connection** - *R. L. Rivest and W. Remmele* |

| | | **A New Model for Inductive Inference** - *R. L. Rivest and R. Sloan* |

| | | **Exploiting Chaos to Predict the Future and Reduce Noise** - *J. D. Farmer and J. J. Sidorowich* |

| April | | **Queries and Concept Learning** - *D. Angluin* |

| May | | **Supervised learning and systems with excess degrees of freedom** - *M. I. Jordan* |

| | | **Diversity-Based Inference of Finite Automata** - *R. E. Schapire* |

| June | | **Two New Frameworks for Learning** - *B. K. Natarajan and P. Tadepalli* |

| July | | **Nonuniform Learnability** - *G. M. Benedek and A. Itai* |

| | | **Automatic Pattern Recognition: A Study of the Probability of Error** - *L. Devroye* |

| 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* |

| | | **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* |