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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Proceedings ArticleDOI
On restricted-focus-of-attention learnability of Boolean functions
TL;DR: An information-theoretic characterization of k-RFA learnability is developed upon which a general tool for proving hardness results are built, and it is shown that, unlike the PAC model, weak learning does not imply strong learning in thek -RFA model.
Journal ArticleDOI
The Vapnik-Chervonenkis dimension of a random graph
TL;DR: The main result gives the exact threshold function for a random graph G ( n, p) to have VC dimension d, which is defined to be the largest cardinality of a shattered set of vertices.
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Functional-type single-input-rule-modules connected neural fuzzy system for wind speed prediction
TL;DR: A novel neural fuzzy method for the hourly wind speed prediction that can be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.
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Simulated annealing approach in backpropagation
TL;DR: Two ways of embedding simulated annealing to improve the usual gradient descent method for the achievement of good minima of the error function are checked.
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Learning robots interacting with humans: from epistemic risk to responsibility
TL;DR: A broad framework is outlined for ethically motivated scientific inquiries, which aim at improving the authors' capability to understand, anticipate, and selectively cope with harmful errors by learning robots.
References
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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.
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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.
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Pattern Classification and Scene Analysis.
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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.