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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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TLDR
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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Supervised Learning of Places from Range Data using AdaBoost
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Practicality of accelerometer side channels on smartphones
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On the Probabilistic Foundations of Probabilistic Roadmap Planning.
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What Can We Learn Privately
TL;DR: This work investigates learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals.