Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 41
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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 , . . . , fм are functions of the xi , i 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 , . . . , fм are functions of the xi , i d . The first d ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |