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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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Journal ArticleDOI
On the Probabilistic Foundations of Probabilistic Roadmap Planning
TL;DR: It is shown that the success of PRM planning depends mainly and critically on favorable “visibility” properties of a robot’s configuration space and a promising direction for speeding up PRM planners is to infer partial knowledge from both workspace geometry and information gathered during roadmap construction, and to use this knowledge to adapt the probability measure for sampling.
Journal ArticleDOI
Programming by Demonstration Using Version Space Algebra
TL;DR: This work formalizes programming by demonstration as a machine learning problem: given the changes in the application state that result from the user's demonstrated actions, learn the general program that maps from one application state to the next.
BookDOI
An Introduction to Machine Learning
TL;DR: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
Journal ArticleDOI
Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization
TL;DR: A stochastic clustering algorithm which uses pairwise similarity of elements and shows how it can be used to address various problems in computer vision, including the low-level image segmentation, mid-level perceptual grouping, and high- level image database organization is presented.
Book ChapterDOI
Detection of Interactive Stepping Stones: Algorithms and Confidence Bounds
TL;DR: In this paper, the authors proposed and analyzed algorithms for stepping-stone detection using ideas from Computational Learning Theory and the analysis of random walks, and achieved provable (polynomial) upper bounds on the number of packets needed to confidently detect and identify encrypted stepping-stones.