Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
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TLDR
This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.Abstract:
Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.read more
Citations
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Journal ArticleDOI
Randomness in generalization ability: a source to improve it
TL;DR: A novel method for measuring generalization ability is defined and it has been shown that if correct classification probability of a single network is greater than half, then as the number of networks in a voting network is increased so does its generalized ability.
Journal ArticleDOI
Optimally-smooth adaptive boosting and application to agnostic learning
TL;DR: A new boosting algorithm is described that is the first such algorithm to be both smooth and adaptive, and the construction of a boosting "tandem" whose asymptotic number of iterations is the lowest possible and whose smoothness is optimal in terms of O(·).
Proceedings ArticleDOI
Learning from a consistently ignorant teacher
TL;DR: A formal model of learning is introduced capturing the idea that teachers may have gaps in their knowledge, and it is shown that knowledge gaps make learning conjunctions of Horn clauses as hard as learning DNF.
Journal ArticleDOI
Partial observability and learnability
TL;DR: The principled and general treatment of missing information during learning is argued to allow an agent to employ learning entirely autonomously, without relying on the presence of an external teacher, as is the case in supervised learning.
Proceedings ArticleDOI
On PAC learning using Winnow, Perceptron, and a Perceptron-like algorithm
TL;DR: This paper analyzes the PAC learning abilities of several simple iterative algorithms for learning linear threshold functions, and shows that the Perceptron algorithm can efficiently PAC learn the class of nested functions under the uniform distribution on boolean examples.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book
The Art of Computer Programming
TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.