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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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Statistical Approach to Ordinal Classification with Monotonicity Constraints
TL;DR: This paper proposes a procedure for “monotonizing” the data by relabeling objects, based on minimization of the empirical risk in the class of all monotone functions, and uses this procedure as a preprocessing tool, improving the accuracy of the classifiers.
BookDOI
Engineering Trustworthy Software Systems
TL;DR: This presentation introduced some Type of Query Query in symbolic form Satisfiability φ sat, unsat, timeout Certificates φ model, proof, unsats core Interpolation.
Proceedings ArticleDOI
On Sample-Based Testers
Oded Goldreich,Dana Ron +1 more
TL;DR: This work advances the study of sample-based property testers by providing several general positive results as well as by revealing relations between variants of this testing model, and shows that certain types of query-based testers yield sample- based testers of sublinear sample complexity.
Proceedings Article
Protocols for Learning Classifiers on Distributed Data
TL;DR: In this article, the authors consider the problem of learning classifiers for labeled data that has been distributed across several nodes and present several sampling-based solutions as well as some two-way protocols which have a provable exponential speed-up over any one-way protocol.
Journal ArticleDOI
Inferring regular languages and ω-languages
TL;DR: This paper surveys residual models for regular languages and ω-languages and the learning algorithms that can infer these models.