Open AccessBook
An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Efficient nonparametric n -body force fields from machine learning
TL;DR: The authors present a scheme to construct classical $n$-body force fields using Gaussian Process (GP) Regression, appropriately mapped over explicit n-body functions (M-FFs), which are as fast as classical parametrized potentials, since they avoid lengthy summations over database entries or weight parameters.
Book ChapterDOI
Introduction to Active Automata Learning from a Practical Perspective
TL;DR: This chapter gives an introduction to active learning of Mealy machines, an automata model particularly suited for modeling the behavior of realistic reactive systems.
Dissertation
Learning from ambiguity
Oded Maron,Tomás Lozano-Pérez +1 more
TL;DR: This thesis will examine one framework of learning from ambiguous examples known as Multiple-Instance learning, which has developed a measure called Diverse Density and algorithms for learning from multiple-instance examples and applied these techniques to problems in drug design, stock prediction, and image database retrieval.
On growing better decision trees from data
TL;DR: Two forms of data massaging, domain-independent and domain-specific, are distinguished and a new framework is outlined for the former, and the importance of the latter is illustrated in the context of two new, complex classification problems in astronomy.
Journal ArticleDOI
Equivalences and Separations Between Quantum and Classical Learnability
TL;DR: These results contrast known results that show that testing black-box functions for various properties, as opposed to learning, can require exponentially more classical queries than quantum queries.