Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... important role in the classification to be performed . At best the process can make use of known information about some measurements that are certain to be important . A weather forecaster in the northern hemisphere might know , for ...
... important role in the classification to be performed . At best the process can make use of known information about some measurements that are certain to be important . A weather forecaster in the northern hemisphere might know , for ...
Sivu 28
... important application of quadric surfaces . 2.10 Implementation of quadric discriminant functions There are two important methods of implementing quadric discriminant functions . One is suggested by Eq . ( 2 · 21 ) and will be of importance ...
... important application of quadric surfaces . 2.10 Implementation of quadric discriminant functions There are two important methods of implementing quadric discriminant functions . One is suggested by Eq . ( 2 · 21 ) and will be of importance ...
Sivu 29
... importance and is discussed in detail in the Appendix . To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , . . . ƒм are functions of the xi , i 1 ,. d . The first d ...
... importance and is discussed in detail in the Appendix . To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , . . . ƒм are functions of the xi , i 1 ,. d . The first d ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 step subsidiary discriminant Suppose terns 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 |