Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 3
Sivu 22
... weight whose value wa + 1 is energized by a signal of +1 . Usually this +1 signal is associated with the pattern as ... vector w , with components wi , w2 , wa . The hyperplane equation can then be written as X • W = -Wd + 1 • " ( 2.12 ) Let ...
... weight whose value wa + 1 is energized by a signal of +1 . Usually this +1 signal is associated with the pattern as ... vector w , with components wi , w2 , wa . The hyperplane equation can then be written as X • W = -Wd + 1 • " ( 2.12 ) Let ...
Sivu 27
... weight which is not a coefficient Wij wi Wjk Wa + 1 Equation ( 2 · 21 ) can be put into matrix form after making the ... vector B = have components given by ba bi = Wj j = 1 , d ... " Let the scalar C = Wd + 1 . Then g ( X ) = XAX + XB + C ( ...
... weight which is not a coefficient Wij wi Wjk Wa + 1 Equation ( 2 · 21 ) can be put into matrix form after making the ... vector B = have components given by ba bi = Wj j = 1 , d ... " Let the scalar C = Wd + 1 . Then g ( X ) = XAX + XB + C ( ...
Sivu 66
... weights wi , w2 , ... wa , wa + 1 . Training a TLU to dichotomize correctly the training subsets is equivalent to find- ing a set of weights such that the hyperplane separates X1 and X2 . The training methods to be described here call ...
... weights wi , w2 , ... wa , wa + 1 . Training a TLU to dichotomize correctly the training subsets is equivalent to find- ing a set of weights such that the hyperplane separates X1 and X2 . The training methods to be described here call ...
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 |