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
Computational learning theory: survey and selected bibliography
TL;DR: The general AI meetings, AAAI and IJCAI, currently have a large number of papers devoted to learning, as do the neural net meetings, and the European meetings on Analogical and Inductive Inference and the AI machine learning communit y’s annual International Conference on Machine Learning.
Journal ArticleDOI
Power of data in quantum machine learning
Hsin-Yuan Huang,Hsin-Yuan Huang,Michael Broughton,Masoud Mohseni,Ryan Babbush,Sergio Boixo,Hartmut Neven,Jarrod R. McClean +7 more
TL;DR: This work shows that some problems that are classically hard to compute can be easily predicted by classical machines learning from data and proposes a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.
Journal ArticleDOI
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
TL;DR: This paper shows that with an infinite signature the higher-order dyadic datalog class H22 has universal Turing expressivity though H^2_2$$H22 is decidable given a finite signature, and generalises the approach of meta-interpretive learning (MIL) to that of learning higher- order dyadicdatalog programs.
Journal ArticleDOI
Learning Conjunctions of Horn Clauses
TL;DR: In this paper, an algorithm for learning the class of Boolean formulas that are expressible as conjunctions of Horn clauses is presented, which uses equivalence queries and membership queries to produce a formula that is logically equivalent to the unknown formula to be learned.
Journal ArticleDOI
Learning intersections and thresholds of halfspaces
TL;DR: This work gives the first polynomial time algorithm to learn any function of a constant number of halfspaces under the uniform distribution to within any constant error parameter.
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.
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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.
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.