Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 15
Sivu 69
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
Sivu 75
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
Sivu 126
... presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory Analysis , Nonparametric Discrimination : Consistency Properties , Project 21-49-004 , Report 4 , prepared at the University of ...
... presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory Analysis , Nonparametric Discrimination : Consistency Properties , Project 21-49-004 , Report 4 , prepared at the University of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 |