Towards realistic theories of learning
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[1] Naoki Abe,et al. Efficient distribution-free population learning of simple concepts , 1994, Annals of Mathematics and Artificial Intelligence.
[2] Naoki Abe,et al. On the computational complexity of approximating distributions by probabilistic automata , 1990, Machine Learning.
[3] D. Haussler,et al. Stochastic context-free grammars for modeling RNA , 1993, 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences.
[4] Naoki Abe,et al. A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars , 1994, ICML.
[5] Naoki Abe,et al. Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries , 1995, ALT.
[6] Naoki Abe,et al. The “lob-pass” problem and an on-line learning model of rational choice , 1993, COLT '93.
[7] H. Sebastian Seung,et al. Learning from a Population of Hypotheses , 1993, COLT '93.
[8] B. Rost,et al. Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.
[9] David Haussler,et al. Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families , 1993, ISMB.
[10] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[11] Yves Schabes,et al. Stochastic Lexicalized Tree-adjoining Grammars , 1992, COLING.
[12] N. Abe. On the computational compulexity of approximating probability distributions by probabilistic automata , 1992 .
[13] Dana Angluin,et al. When won't membership queries help? , 1991, STOC '91.
[14] C. Sander,et al. Database of homology‐derived protein structures and the structural meaning of sequence alignment , 1991, Proteins.
[15] Akihiko Konagaya,et al. Learning Stochastic Motifs from Genetic Sequences , 1991, ML.
[16] Robert E. Schapire,et al. Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.
[17] Robert E. Schapire,et al. On the sample complexity of weak learning , 1990, COLT '90.
[18] Kenji Yamanishi,et al. A learning criterion for stochastic rules , 1990, COLT '90.
[19] Daniel N. Osherson,et al. Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists , 1990 .
[20] R. Herrnstein. Rational Choice Theory Necessary but Not Sufficient , 1990 .
[21] Robert H. Sloan,et al. Computational learning theory: new models and algorithms , 1989 .
[22] Jeffrey Scott Vitter,et al. Learning in parallel , 1988, COLT '88.
[23] Philip D. Laird,et al. Efficient unsupervised learning , 1988, COLT '88.
[24] Naoki Abe,et al. Feasible Learnability of Formal Grammars and The Theory of Natural Language Acquisition , 1988, COLING.
[25] J. Rissanen. Stochastic Complexity and Modeling , 1986 .
[26] Aravind K. Joshi,et al. Some Computational Properties of Tree Adjoining Grammars , 1985, ACL.
[27] D. Pollard. Convergence of stochastic processes , 1984 .
[28] L. R. Rabiner,et al. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.
[29] Azaria Paz,et al. Introduction to Probabilistic Automata , 1971 .