Download An Introduction to Computational Learning Theory by Michael J. Kearns PDF

By Michael J. Kearns

Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a couple of valuable issues in computational studying thought for researchers and scholars in man made intelligence, neural networks, theoretical computing device technology, and statistics.Computational studying thought is a brand new and speedily increasing quarter of study that examines formal types of induction with the objectives of studying the typical tools underlying effective studying algorithms and choosing the computational impediments to learning.Each subject within the ebook has been selected to explain a normal precept, that is explored in an actual formal surroundings. instinct has been emphasised within the presentation to make the fabric obtainable to the nontheoretician whereas nonetheless supplying targeted arguments for the professional. This stability is the results of new proofs of verified theorems, and new shows of the traditional proofs.The themes coated comprise the incentive, definitions, and basic effects, either optimistic and unfavorable, for the generally studied L. G. Valiant version of doubtless nearly right studying; Occam's Razor, which formalizes a courting among studying and information compression; the Vapnik-Chervonenkis size; the equivalence of vulnerable and powerful studying; effective studying within the presence of noise via the tactic of statistical queries; relationships among studying and cryptography, and the ensuing computational obstacles on effective studying; reducibility among studying difficulties; and algorithms for studying finite automata from lively experimentation.

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CI' b,) and a bit b, Xl," • ,Xn is in which each Ci conjunction of at most k literals over XlJ , Xn, and each bi E {O, I}. For any input a E {o,l}n, the value L(a) is defined to be bi, where j is a • is the smallest index sati sfy ing Cj (a) L(a) = • . 1; if no such index exists , then the end of the list. We call bi the bit associated with the condition Ci. 1 shows ::;:: b. Thus, b is the "default" value in case a falls off an example of a 2-decision list along with its evaluation on a particular input.

Let c be a concept over X. Then we say that c is consistent with 8 (or equivalently, 8 is consistent with c) if for aliI::; i ::; m, C(Xi) = bi. Before detailing our choice for the NP-complete language A and the mapping of � to So, just suppose for now that we have managed to arrange things so that a E A if and only if 80 is consistent with some con cept in C. We now show how a PAC learn ing algorithm L for C can be used to determine if th ere exists a conce pt in C that is consistent with 80 (and thus whether a E A) with high probability.

A representation class. n•m that is consistent with S. n that with probability at least 1 6 o beys error (h) � E. - Note that here we do not necessarily claim that L is an effic i ent PAC learning algorithm. n•ml. Moreover, since the running time of L has a polynomial dependence on m, in order Copyrighted Material Chapter 2 36 to assert that L is an efficient PAC algorithm, we also have to bound m by some polynomial in n, size(c), lIe and l/f>. tn,ml grows only as mP, and therefore given any e, this is smaller than bem for a small value of m.

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