Haussler, David

26 publications

AISTATS 1999 Probabilistic Kernel Regression Models Tommi S. Jaakkola, David Haussler

NeurIPS 1998 Exploiting Generative Models in Discriminative Classifiers Tommi Jaakkola, David Haussler

COLT 1997 A Brief Look at Some Machine Learning Problems in Genomics David Haussler

MLJ 1996 Rigorous Learning Curve Bounds from Statistical Mechanics David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby

COLT 1995 General Bounds on the Mutual Information Between a Parameter and N Conditionally Independent Observations David Haussler, Manfred Opper

MLJ 1994 Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension David Haussler, Michael J. Kearns, Robert E. Schapire

COLT 1994 Rigorous Learning Curve Bounds from Statistical Mechanics David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby

COLT 1991 Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension David Haussler, Michael J. Kearns, Robert E. Schapire

COLT 1991 Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron with Noise Manfred Opper, David Haussler

NeurIPS 1991 Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods David Haussler, Michael Kearns, Manfred Opper, Robert Schapire

NeurIPS 1991 Unsupervised Learning of Distributions on Binary Vectors Using Two Layer Networks Yoav Freund, David Haussler

MLJ 1990 Boolean Feature Discovery in Empirical Learning Giulia Pagallo, David Haussler

ALT 1990 Decision Theoretic Generalizations of the PAC Learning Model David Haussler

AAAI 1990 Probably Approximately Correct Learning David Haussler

COLT 1989 Informed Parsimonious Inference of Prototypical Genetic Sequences Aleksandar Milosavljevic, David Haussler, Jerzy Jurka

MLJ 1989 Learning Conjunctive Concepts in Structural Domains David Haussler

ICML 1989 Two Algorithms That Learn DNF by Discovering Relevant Features Giulia Pagallo, David Haussler

NeCo 1989 What Size Net Gives Valid Generalization? Eric B. Baum, David Haussler

COLT 1988 A General Lower Bound on the Number of Examples Needed for Learning Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant

COLT 1988 Equivalence of Models for Polynomial Learnability David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth

COLT 1988 Learning Decision Trees from Random Examples Andrzej Ehrenfeucht, David Haussler

COLT 1988 Predicting 0, 1-Functions on Randomly Drawn Points David Haussler, Nick Littlestone, Manfred K. Warmuth

NeurIPS 1988 What Size Net Gives Valid Generalization? Eric B. Baum, David Haussler

AAAI 1987 Learning Conjunctive Concepts in Structural Domains David Haussler

MLJ 1987 New Theoretical Directions in Machine Learning David Haussler

AAAI 1986 Quantifying the Inductive Bias in Concept Learning (Extended Abstract) David Haussler