Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Feasible learnability of formal grammars and the theory of natural language acquisition
TL;DR: A novel, nontrivial constraint on the degree of "locality" of grammars which allows a rich class of mildly context sensitive languages to be feasibly learnable is presented.
Book ChapterDOI
Approximating the Volume of General Pfaffian Bodies
TL;DR: A new powerful method of approximating the volume (and integrals) of vast number of geometric bodies defined by boolean combinations of Pfaffian conditions, which depends on the polynomial bounds on the VC - Dimensions of the classes of sets to be measured.
Book ChapterDOI
What is evolvability
TL;DR: It can be concluded that Evolvability is a property of a lineage because there is a concrete hypothesis which explains the limits of volvox disparity.
BookDOI
Biological and Artificial Intelligence Environments
TL;DR: The book reports the proceedings of the 15th Italian workshop on neural networks issued by the Italian Society on Neural Networks SIREN, and attracts contributions from foreign researchers as well, so that the reader may capture the flavor of the state of the art in the international community.
Journal ArticleDOI
Sample Sizes for Threshold Networks with Equivalences
TL;DR: It is shown that the sample site for reliable learning can be bounded above by a formula similar to that required for single output networks with no equivalences.
References
More filters
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.