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
More filters
PAC-Learning in the Presence of One-sided Classification Noise.
TL;DR: In this paper, the authors derived an upper and a lower bound on the sample size needed for PAC-learning a concept class in the presence of one-sided classification noise, which is achieved by the strategy "Minimum One-sided Disagreement" up to a logarithmic factor.
Book ChapterDOI
Computability of convex sets
Martin Kummer,Marcus Schäfer +1 more
TL;DR: In this article, the authors investigated the computability of convex sets restricted to rational inputs, and showed the existence of effective approximations by polygons or effective line intersection tests.
Concept drift learning and its application to adaptive information filtering
TL;DR: This dissertation presents a Mbultiple Tbhree-Dbescriptor Rbepresentation (MTDR) algorithm, a novel algorithm for learning concept drift especially built for tracking the dynamics of multiple target concepts in the information filtering domain.
Proceedings ArticleDOI
Generalization-Aware Structured Regression towards Balancing Bias and Variance
TL;DR: The Generalization-Aware Collaborative Ensemble Regressor (GLACER) is developed, a model that bags a crowd of structured regression models, while allowing them to collaborate in a fashion that minimizes the proposed objective function.
Proceedings ArticleDOI
On the complexity of learning minimum time-bounded Turing machines
TL;DR: In this article, the authors studied time-bounded program-size complexity and showed that for certain variations of these problems, they could be either polynomial-time computable or not polynomially time computable, depending on different oracles.
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.