Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 44
... functions . Central to the decision - theoretic treatment is the specifi- cation of a loss function , λ ( i | j ) . Here λ ( ij ) is a function defined for i 1 , R and represents the loss incurred when the machine places a pattern ...
... functions . Central to the decision - theoretic treatment is the specifi- cation of a loss function , λ ( i | j ) . Here λ ( ij ) is a function defined for i 1 , R and represents the loss incurred when the machine places a pattern ...
Sivu 45
... loss of λ ( ij ) , where j is the actual category of pattern X. The ... function , we can obtain a set of equiva- lent , but simpler , discriminant ... function of j , p ( X | j ) is often called the likelihood of j with respect to X ; p ...
... loss of λ ( ij ) , where j is the actual category of pattern X. The ... function , we can obtain a set of equiva- lent , but simpler , discriminant ... function of j , p ( X | j ) is often called the likelihood of j with respect to X ; p ...
Sivu 46
... loss function We have shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are ... loss function , the discriminant functions can be 46 PARAMETRIC TRAINING METHODS.
... loss function We have shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are ... loss function , the discriminant functions can be 46 PARAMETRIC TRAINING METHODS.
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 |