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
Nonparametric estimation and classification using radial basis function nets and empirical risk minimization
TL;DR: The authors show the optimal nets to be consistent in the problem of nonlinear function approximation and in nonparametric classification in RBF networks and obtain the network parameters through empirical risk minimization.
Journal ArticleDOI
Catching elephants with mice: Sparse sampling for monitoring sensor networks
TL;DR: The overall simplicity and generality of the technique suggests that it is 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
Why Attackers Win: On the Learnability of XOR Arbiter PUFs
TL;DR: A respective PAC learning framework is presented, able to establish a theoretical limit on the number of arbiter chains, where an XOR arbiter PUF can be learned in polynomial time, with given levels of accuracy and confidence.
Journal ArticleDOI
The Perceptron algorithm versus Winnow: linear versus logarithmic mistake bounds when few input variables are relevant
TL;DR: It is shown that an adversary can force any additive algorithm to make (N + k −1) 2 mistakes in learning a monotone disjunction of at most k literals and that for k ⪡ N, Winnow clearly outperforms the Perceptron algorithm also on nonadversarial random data.
Dissertation
Timing is of the essence : perceptual and computational techniques for representing, learning, and reproducing expressive timing in percussive rhythm
TL;DR: Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1993.
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.