Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu vii
... properties of various discriminant functions or to find methods for their selection or adjustment . The following topics are given special treatment : 1. Parametric and nonparametric training methods . The decision- theoretic approach ...
... properties of various discriminant functions or to find methods for their selection or adjustment . The following topics are given special treatment : 1. Parametric and nonparametric training methods . The decision- theoretic approach ...
Sivu 15
... PROPERTIES AND THEIR IMPLEMENTATIONS 2.1 Families of discriminant functions The task of selecting a discriminant function for use in a pattern - classi- fying machine is simplified by first limiting the class of functions from which the ...
... PROPERTIES AND THEIR IMPLEMENTATIONS 2.1 Families of discriminant functions The task of selecting a discriminant function for use in a pattern - classi- fying machine is simplified by first limiting the class of functions from which the ...
Sivu 103
... properties of layered machines We have seen in Secs . 6.2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation , to be discussed ...
... properties of layered machines We have seen in Secs . 6.2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation , to be discussed ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |