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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Book
Towards Automated System Synthesis Using SCIDUCTION
Sanjit A. Seshia,Susmit Jha +1 more
TL;DR: It is illustrated that the proposed synthesis approach can be used to automate system synthesis, and thus, can prove to be an effective aid to designers and developers.
Journal ArticleDOI
Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks
Gengchen Liu,Kaiqi Zhang,Xiaoliang Chen,Hongbo Lu,Jiannan Guo,Jie Yin,Roberto Proietti,Zuqing Zhu,S. J. Ben Yoo +8 more
TL;DR: This paper demonstrates, for the first time to the authors' knowledge, hierarchical learning framework for inter-domain service provisioning in software-defined elastic optical networking (EON) using a broker-based hierarchical architecture.
Journal ArticleDOI
Vapnik-chervonenkis dimension bounds for two-and three-layer networks
TL;DR: It is shown that the Vapnik-Chervonenkis dimension of the class of functions that can be computed by arbitrary two-layer or some completely connected three-layer threshold networks with real inputs is at least linear in the number of weights in the network.
Proceedings ArticleDOI
Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks
TL;DR: It is shown that the Vapnik-Chervonenkis dimension of the class of functions that can be computed by a two-layer threshold network with real inputs is at least proportional to the number of weights in the network.
Dissertation
Fundamental Limitations of Semi-Supervised Learning
TL;DR: This thesis develops a framework under which one can analyze the potential benefits, as measured by the sample complexity of semi-supervised learning, and concludes that unless the learner is absolutely certain there is some non-trivial relationship between labels and the unlabeled distribution, semi- supervised learning cannot provide significant advantages over supervised learning.
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.
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.