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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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Fast and Scalable Local Kernel Machines
Nicola Segata,Enrico Blanzieri +1 more
TL;DR: It is shown that locality can be an important factor to sensibly speed-up learning approaches and kernel methods, differently from other recent techniques that tend to dismiss local information in order to improve scalability.
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Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems
Sergio Yovine,Yassine Lakhnech +1 more
Inductive learning of phonotactic patterns
TL;DR: Of the Dissertation Inductive Learning of Phonotactic Patterns and its Applications to Teaching and Research: Foundations of a Response to the Response to Tocqueville's inequality.
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Generating models of infinite-state communication protocols using regular inference with abstraction
TL;DR: A framework is presented, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets, and generated models of SIP and TCP components.
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Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model
TL;DR: The problem of identifying a genetic network from the data obtained by multiple gene disruptions and overexpressions in regard to the number of experiments and the complexity of experiments is analyzed.