Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 30
Sivu 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.
... 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 76
... implement any dichotomy of X that can possibly be implemented using a discriminant function belonging to the same family . The training of a machine is accomplished by training only the linear part ; the processor remains fixed ...
... implement any dichotomy of X that can possibly be implemented using a discriminant function belonging to the same family . The training of a machine is accomplished by training only the linear part ; the processor remains fixed ...
Sivu 129
... implementation employing weights and summers . The term QX2 is computed by summing the squares of the outputs of p1 summers , and Q , X2 is computed by summing the squares of the outputs of p2 summers . The implementation shown in Fig ...
... implementation employing weights and summers . The term QX2 is computed by summing the squares of the outputs of p1 summers , and Q , X2 is computed by summing the squares of the outputs of p2 summers . The implementation shown in Fig ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |