Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
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
Assessing Generalization Ability of Majority Vote Point Classifiers
TL;DR: It is shown that a class of classifiers named majority vote point (MVP) classifiers, on account of very low VC dimension, can exhibit a generalization error that is even lower than that of linear classifiers.
Proceedings ArticleDOI
Catching elephants with mice: sparse sampling for monitoring sensor networks
TL;DR: The overall simplicity and generality of the technique suggests that it may be well-suited for a wide class of sensornet applications, including monitoring of physical environments, network anomalies, network security, or any abstract binary event that affects a significant number of nodes in the network.
Book ChapterDOI
The VC-dimension of SQL queries and selectivity estimation through sampling
TL;DR: A novel method is developed, based on the statistical concept of VC-dimension, for evaluating the selectivity (output cardinality) of SQL queries - a crucial step in optimizing the execution of large scale database and data-mining operations.
Proceedings ArticleDOI
Occam's razor for functions
TL;DR: It is shown that the existence of an Om approximation is sufficient to guarantee the probably approximate learnability of classes of functions on the reals even in the presence of arbkwily large but random additive noise.
Journal ArticleDOI
Monitoring anytime algorithms
TL;DR: This paper analyzes the issues involved in run-time monitoring of anytime algorithms and casts the problem in a new framework from which some improved monitoring strategies emerge.
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.