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
Almost optimal set covers in finite VC-dimension
TL;DR: A deterministic polynomial-time method for finding a set cover in a set system (X, ℛ) of dual VC-dimensiond such that the size of the authors' cover is at most a factor ofO(d log(dc)) from the optimal size,c.
Journal ArticleDOI
Learning Boolean concepts in the presence of many irrelevant features
TL;DR: Five algorithms that identify a subset of features sufficient to construct a hypothesis consistent with the training examples are presented and it is shown that any learning algorithm implementing the MIN-FEATURES bias requires ⊖(( ln ( l δ ) + [2 p + p ln n])/e) training examples to guarantee PAC-learning a concept having p relevant features out of n available features.
Journal ArticleDOI
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
TL;DR: It is shown that the notion of inductive bias in concept learning can be quantified in a way that directly relates to learning performance in the framework recently introduced by Valiant.
Book ChapterDOI
Exploiting Task Relatedness for Multiple Task Learning
TL;DR: This work offers an alternative approach to multiple task learning, defining relatedness of tasks on the basis of similarity between the example generating distributions that underline these task.
Journal ArticleDOI
PUF Modeling Attacks on Simulated and Silicon Data
Ulrich Rührmair,Jan Sölter,Frank Sehnke,Xiaolin Xu,Ahmed Mahmoud,Vera Stoyanova,Gideon Dror,Jürgen Schmidhuber,Wayne Burleson,Srinivas Devadas +9 more
TL;DR: Numerical modeling attacks on several proposed strong physical unclonable functions (PUFs) are discussed, leading to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike.
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.
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.