Random generation of combinatorial structures from a uniform
Theoretical Computer Science
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
On learning embedded symmetric concepts
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The weighted majority algorithm
Information and Computation
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Predicting Nearly As Well As the Best Pruning of a Decision Tree
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Journal of the ACM (JACM)
Efficient learning with virtual threshold gates
Information and Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Agnostic learning of geometric patterns
Journal of Computer and System Sciences
On the Boosting Pruning Problem
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Predicting Nearly as well as the best Pruning of a Planar Decision Graph
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Random Walks on Truncated Cubes and Sampling 0-1 Knapsack Solutions
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Multiple-Instance Learning of Real-Valued Geometric Patterns
Annals of Mathematics and Artificial Intelligence
Online closure-based learning of relational theories
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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We explore applications of Markov chain Monte Carlo methods for weight estimation over inputs to the Weighted Majority (WM) and Winnow algorithms. This is useful when there are exponentially many such inputs and no apparent means to efficiently compute their weighted sum. The applications we examine are pruning classifier ensembles using WM and learning general DNF formulas using Winnow. These uses require exponentially many inputs, so we define Markov chains over the inputs to approximate the weighted sums. We state performance guarantees for our algorithms and present preliminary empirical results.