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 A ( 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 A ( 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 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |