Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 72
... weight vectors produced by the training procedure con- verges toward a solution weight vector . The fixed - increment and absolute correction rules are guaranteed to produce a solution weight vector after only a finite number of ...
... weight vectors produced by the training procedure con- verges toward a solution weight vector . The fixed - increment and absolute correction rules are guaranteed to produce a solution weight vector after only a finite number of ...
Sivu 75
... product of a weight vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) . Y for i = 1 , R · " ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine ...
... product of a weight vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) . Y for i = 1 , R · " ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine ...
Sivu 89
... vectors from the training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD ... solution weight vectors WR ; then observe that Z is linearly contained with an RD- dimensional vector V ( W1 ...
... vectors from the training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD ... solution weight vectors WR ; then observe that Z is linearly contained with an RD- dimensional vector V ( W1 ...
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 |