Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 32
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... implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in ... implemented. FIGURE 2.8 A quadric discriminator. SOME IMPORTANT DISCRIMINANT FUNCTIONS 29.
... implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in ... implemented. FIGURE 2.8 A quadric discriminator. SOME IMPORTANT DISCRIMINANT FUNCTIONS 29.
Sivu 40
... implemented by machine Capacity Hyperplane 2 ( d + 1 ) Hypersphere 2 ( d + 2 ) General quadric surface rth - order ... implementation of a quadric machine ( Fig . 2-8 ) was proposed by Koford . The material on § functions is based on the ...
... implemented by machine Capacity Hyperplane 2 ( d + 1 ) Hypersphere 2 ( d + 2 ) General quadric surface rth - order ... implementation of a quadric machine ( Fig . 2-8 ) was proposed by Koford . The material on § functions is based on the ...
Sivu 129
... implementation employing weights and summers . The term Q1X2 is computed by summing the squares of the outputs of p1 summers , and Q2X2 is computed by summing the squares of the outputs of p2 summers . The implementation shown in Fig ...
... implementation employing weights and summers . The term Q1X2 is computed by summing the squares of the outputs of p1 summers , and Q2X2 is computed by summing the squares of the outputs of p2 summers . The implementation shown in Fig ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |