Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 54
Sivu 72
... example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern . hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU ...
... example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern . hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU ...
Sivu 101
... example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The geometrical interpretation of this ...
... example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The geometrical interpretation of this ...
Sivu 105
... example , pat- terns 3 and 7 both yield a response of +1 for TLU 1 and a response of -1 for the other TLUS ; hence these two patterns are transformed into the single point ( 1 , -1 , -1 ) in image space . The numbers associated with the ...
... example , pat- terns 3 and 7 both yield a response of +1 for TLU 1 and a response of -1 for the other TLUS ; hence these two patterns are transformed into the single point ( 1 , -1 , -1 ) in image space . The numbers associated with the ...
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 |