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
Multiple Comparisons in Induction Algorithms
David Jensen,Paul R. Cohen +1 more
TL;DR: This work analyzes the statistical properties of MCPs and shows how failure to adjust for these properties leads to the pathologies of induction algorithms, including attribute selection errors, overfitting, and oversearching.
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A statistical approach to learning and generalization in layered neural networks
TL;DR: The proposed formalism is applied to the problems of selecting an optimal architecture and the prediction of learning curves and the Gibbs distribution on the ensemble of networks with a fixed architecture is derived.
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An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution
TL;DR: The strong learning algorithm utilizes one of Freund's boosting techniques and relies on the fact that boosting does not require a completely distribution-independent weak learner, and it is shown that DNF is weakly learnable with respect to uniform from noisy examples.
Book ChapterDOI
Inductive Inference, DFAs, and Computational Complexity
TL;DR: The results discussed determine the extent to which DFAs can be feasibly inferred, and highlight a number of interesting approaches in computational learning theory.
Journal ArticleDOI
On randomized one-round communication complexity
Ilan Kremer,Noam Nisan,Dana Ron +2 more
TL;DR: The results include a connection to the VC-dimension, a study of the problem of computing the inner product of two real valued vectors, and a relation between “simultaneous” protocols and one-round protocols.
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.