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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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On the learnability of monotone functions
Rocco A. Servedio,Homin K. Lee +1 more
TL;DR: This thesis shows that Boolean functions computed by polynomial-size monotone circuits are hard to learn assuming the existence of one-way functions, and shows that non-monotone DNF formulas, juntas, and sparse GF 2 formulas are teachable in the average case.
Uniform Glivenko-Cantelli Theorems and Concentration of Measure in the Mathematical Modelling of Learning
TL;DR: This paper surveys certain developments in the use of probabilistic techniques for the modelling of generalization in machine learning, particularly the use (and derivation of) uniform Glivenko-Cantelli theorems, and theUse of concentration of measure results.
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On the connection between the phase transition of the covering test and the learning success rate in ILP
Erick Alphonse,Aomar Osmani +1 more
TL;DR: It is shown that a top-down data-driven strategy can cross any plateau if near-misses are supplied in the training set, whereas they do not change the plateau profile and do not guide a generate-and-test strategy.