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
Sample Compression Schemes for VC Classes
Shay Moran,Amir Yehudayoff +1 more
TL;DR: It is shown that every concept class C with VC dimension d has a sample compression scheme of size exponential in d, and an approximate minimax phenomenon for binary matrices of low VC dimension is used, which may be of interest in the context of game theory.
Proceedings ArticleDOI
General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
Javed A. Aslam,Scott E. Decatur +1 more
TL;DR: This work derives general bounds on the complexity of learning in the statistical query model and in the PAC model with classification noise by considering the problem of boosting the accuracy of weak learning algorithms which fall within the statisticalquery model.
Proceedings ArticleDOI
A theoretical framework for learning from a pool of disparate data sources
TL;DR: A theoretical framework for making classification predictions from a collection of different data sources, without creating explicit translations between them is proposed, which allows a precise mathematical analysis of the complexity of such tasks, and it provides a tool for the development and comparison of different learning algorithms.
Journal ArticleDOI
A machine discovery from amino acid sequences by decision trees over regular patterns
Setsuo Arikawa,Satoru Miyano,Ayumi Shinohara,Satoru Kuhara,Yasuhito Mukouchi,Takeshi Shinohara +5 more
TL;DR: It is shown that the class of languages defined by decesion trees of depth at mostd overk-variable regular patterns is polynomial-time learnable in the sense of probably approximately correct (PAC) learning for any fixedd, k≥0.
Journal ArticleDOI
Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression
TL;DR: The statistical results prove that the L2-regularized Logistic Regression method is statistically significant better than other three algorithms, which means it could be meaningful to assist physicians efficiently in “real-world” diagnostic situations.
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.