Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 5
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
Sivu 71
... described , the value of c determines how far the weight point is moved . We have distinguished three cases . In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same ...
... described , the value of c determines how far the weight point is moved . We have distinguished three cases . In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same ...
Sivu 125
... described by the phrase " learning without a teacher . " ) The mode - seeking training rule described in Sec . 7-6 was originally proposed and tested by Stark , Okajima , and Whipple . ' They applied the rule in a simulated PWL machine ...
... described by the phrase " learning without a teacher . " ) The mode - seeking training rule described in Sec . 7-6 was originally proposed and tested by Stark , Okajima , and Whipple . ' They applied the rule in a simulated PWL machine ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X 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 point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |