978 - 0 - 521 - 76391 - 2 - Phase Transitions in Machine Learning
نویسندگان
چکیده
ion, 338 animal visual system, 314 aromatic rings, 340 asymptotic behavior, 85–86, 213 attractor, 147, 236, 314, 317 avalanche process, 324 Avogadro number, 16 Axelrod model, 307 backbone, 60–61 backdoor, 61–64 batch setting, 94 Bayes’ decision rule, 95 Bayes error, 95 Bethe lattice, 39 bifurcations, 317 blind spot, 231, 329, 333 Boltzman constant, 19 boolean algebra, 169 attribute, 169 expression, 108 function, 94 matrix, 71 Bose–Einstein gas, 301 canonical ensemble, 21 categorization, 92 cavity graph, 40, 67 cavity method, 35, 39–42, 68, 311 cavity method approach, 39 ChangeToNegative, 225 ChangeToPositive, 225 Church−Turing thesis, 4 classification classifier, 95 problem, 107 rule, 340 clique, 214, 309 k-clique, 302 closed world assumption, 117, 223 cluster of solutions, 59, 315 coexisting opinions, 306 coin tossing, 322, 323 collective behavior, 304, 309 complex systems, 300, 301, 302, 309, 310, 311, 333 complexity computational, 3 covering test, 111, 189, 195 polynomial, 6 space, 5 time, 5 typical, 8 worst case, 5 computational cost, 219, 229 computational grids, 309 concept, 93, 112 approximation, 244, 253 n-arity, 117 0-arity, 116, 129 binary, 113 description of, 339 approximated, 335 k-DNF, 169
منابع مشابه
Phase Transitions in Machine Learning
Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning and of sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon and the extensive experimental investigation that supports its presence. They the...
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تاریخ انتشار 2011