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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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Theory of classification : a survey of some recent advances
TL;DR: The last few years have witnessed important new developments in the theory and practice of pattern classification, see as discussed by the authors for a survey of the main new ideas that have lead to these important recent developments.
Learning with Labeled and Unlabeled Data
TL;DR: A rigorous definition of the problem is given and the crucial role of prior knowledge is put forward, and the important notion of input-dependent regularization is discussed.
Posted Content
What Can We Learn Privately
TL;DR: In this paper, it was shown that a concept class is learnable by a local algorithm if and only if it is learnedable in the statistical query (SQ) model.
Book
Arithmetic Circuits: A Survey of Recent Results and Open Questions
Amir Shpilka,Amir Yehudayoff +1 more
TL;DR: The goal of this monograph is to survey the field of arithmetic circuit complexity, focusing mainly on what it finds to be the most interesting and accessible research directions, with an emphasis on works from the last two decades.
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An analysis of model-based Interval Estimation for Markov Decision Processes
TL;DR: A theoretical analysis of Model-based Interval Estimation and a new variation called MBIE-EB are presented, proving their efficiency even under worst-case conditions.