The mathematics of inheritance systems
The mathematics of inheritance systems
Information Processing Letters
Learning in the presence of malicious errors
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
On the sample complexity of finding good search strategies
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Prediction-preserving reducibility
Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
Learning boolean functions in an infinite attribute space
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Learning in the presence of finitely or infinitely many irrelevant attributes
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Machine Learning
Machine Learning
Adaptive Versus Nonadaptive Attribute-Efficient Learning
Machine Learning
Parallel Attribute-Efficient Learning of Monotone Boolean Functions
SWAT '00 Proceedings of the 7th Scandinavian Workshop on Algorithm Theory
On parallel attribute-efficient learning
Journal of Computer and System Sciences
Covering analysis of the greedy algorithm for partial cover
Algorithms and Applications
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We present an algorithm for learning sets of rules that are organized into up to k levels. Each level can contain an arbitrary number of rules “if c then l” where l is the class associated to the level and c is a concept from a given class of basic concepts. The rules of higher levels have precedence over the rules of lower levels and can be used to represent exceptions. As basic concepts we can use Boolean attributes in the infinite attribute space model, or certain concepts defined in terms of substrings. Given a sample of m examples, the algorithm runs in polynomial time and produces a consistent representation of size O((log m)knk), where n is the size of the smallest consistent representation with k levels of rules. This implies that the algorithm learns in the PAC model. The algorithm repeatedly applies the greedy heuristics for weighted set cover. The weights are obtained from approximate solutions to previous set cover problems.