Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 89
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then applying ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then applying ...
Sivu 92
... proof of Theorem 5.1 was outlined by Rosenblatt.1 Subsequent proofs have been given by Joseph , 2 Block , Charnes , Novikoff , " Single- ton , Ridgway , ' and possibly others . Our Proof 1 follows the method * Since S✩ terminates when ...
... proof of Theorem 5.1 was outlined by Rosenblatt.1 Subsequent proofs have been given by Joseph , 2 Block , Charnes , Novikoff , " Single- ton , Ridgway , ' and possibly others . Our Proof 1 follows the method * Since S✩ terminates when ...
Sivu 134
... proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate ...
... proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |