Computational Learning Theory

[1]  Dana Angluin,et al.  Teachers, learners and black boxes , 1997, COLT '97.

[2]  W. Maass,et al.  Eecient Learning with Virtual Threshold Gates , 1997 .

[3]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[4]  Sally A. Goldman,et al.  Teaching a Smarter Learner , 1996, J. Comput. Syst. Sci..

[5]  Sally A. Goldman,et al.  A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns , 1996, ICML.

[6]  Yishay Mansour,et al.  Simple learning algorithms for decision trees and multivariate polynomials , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[7]  Donna K. Slonim,et al.  Learning with unreliable boundary queries , 1995, COLT '95.

[8]  Javed A. Aslam,et al.  Specification and simulation of statistical query algorithms for efficiency and noise tolerance , 1995, COLT '95.

[9]  Manfred K. Warmuth,et al.  Efficient Learning With Virtual Threshold Gates , 1995, Inf. Comput..

[10]  Robert H. Sloan,et al.  Four Types of Noise in Data for PAC Learning , 1995, Inf. Process. Lett..

[11]  Leslie G. Valiant,et al.  Cryptographic limitations on learning Boolean formulae and finite automata , 1994, JACM.

[12]  N. Fisher,et al.  Probability Inequalities for Sums of Bounded Random Variables , 1994 .

[13]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[14]  Nader H. Bshouty,et al.  Asking questions to minimize errors , 1993, COLT '93.

[15]  David Haussler,et al.  How to use expert advice , 1993, STOC.

[16]  Michael Kearns,et al.  Efficient noise-tolerant learning from statistical queries , 1993, STOC.

[17]  Marek Karpinski,et al.  Learning read-once formulas with queries , 1993, JACM.

[18]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[19]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..

[20]  Martin Anthony,et al.  Computational learning theory: an introduction , 1992 .

[21]  Robert H. Sloan,et al.  Corrigendum to types of noise in data for concept learning , 1988, COLT '92.

[22]  R. Schapire,et al.  Toward efficient agnostic learning , 1992, COLT '92.

[23]  R. Schapire.  Toward Eecient Agnostic Learning , 1992 .

[24]  Balas K. Natarajan,et al.  Machine Learning: A Theoretical Approach , 1992 .

[25]  H. Aizenstein,et al.  Exact learning of read-twice DNF formulas , 1991, [1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science.

[26]  P. Matthews.  Generating a Random Linear Extension of a Partial Order , 1991 .

[27]  Sally A. Goldman,et al.  The Difficulty of Random Attribute Noise , 1991 .

[28]  Leonard Pitt,et al.  Prediction-Preserving Reducibility , 1990, J. Comput. Syst. Sci..

[29]  Michael Frazier,et al.  Learning conjunctions of Horn clauses , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[30]  Avrim Blum,et al.  Separating distribution-free and mistake-bound learning models over the Boolean domain , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[31]  Robert E. Schapire,et al.  Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[32]  Michael Kearns,et al.  Computational complexity of machine learning , 1990, ACM distinguished dissertations.

[33]  Ronald L. Rivest,et al.  Inferring graphs from walks , 1990, COLT '90.

[34]  Ryszard S. Michalski,et al.  Machine learning: an artificial intelligence approach volume III , 1990 .

[35]  Ronald L. Rivest,et al.  Learning binary relations, total orders, and read-once formulas , 1990 .

[36]  Avrim Blum,et al.  Learning boolean functions in an infinite attribute space , 1990, STOC '90.

[37]  Dana Angluin.  Equivalence queries and approximate fingerprints , 1989, COLT '89.

[38]  Ronald L. Rivest,et al.  Learning binary relations and total orders , 1989, 30th Annual Symposium on Foundations of Computer Science.

[39]  Manfred K. Warmuth,et al.  The weighted majority algorithm , 1989, 30th Annual Symposium on Foundations of Computer Science.

[40]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[41]  Ronald L. Rivest,et al.  Inference of finite automata using homing sequences , 1989, STOC '89.

[42]  M. Kearns,et al.  Crytographic limitations on learning Boolean formulae and finite automata , 1989, STOC '89.

[43]  Ming Li,et al.  Learning in the presence of malicious errors , 1993, STOC '88.

[44]  David Haussler,et al.  Predicting {0,1}-functions on randomly drawn points , 1988, COLT '88.

[45]  George Shackelford,et al.  Learning k-DNF with noise in the attributes , 1988, Annual Conference Computational Learning Theory.

[46]  David Haussler,et al.  Equivalence of models for polynomial learnability , 1988, COLT '88.

[47]  Leslie G. Valiant,et al.  A general lower bound on the number of examples needed for learning , 1988, COLT '88.

[48]  Nathan Linial,et al.  Results on learnability and the Vapnik-Chervonenkis dimension , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[49]  Leslie G. Valiant,et al.  Computational limitations on learning from examples , 1988, JACM.

[50]  A. Sinclair.  Randomised algorithms for counting and generating combinatorial structures , 1988 .

[51]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[52]  N. Littlestone.  Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[53]  Silvio Micali,et al.  How to construct random functions , 1986, JACM.

[54]  Leslie G. Valiant,et al.  Learning Disjunction of Conjunctions , 1985, IJCAI.

[55]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[56]  J. Kahn,et al.  Balancing poset extensions , 1984 .

[57]  Temple F. Smith.  Occam's razor , 1980, Nature.

[58]  Leslie G. Valiant,et al.  The Complexity of Computing the Permanent , 1979, Theor. Comput. Sci..

[59]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[60]  Leslie G. Valiant,et al.  Fast probabilistic algorithms for hamiltonian circuits and matchings , 1977, STOC '77.

[61]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[62]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[63]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[64]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.