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 ( ij ) . Here λ ( ij ) is a function defined for i = 1 , R and j R and represents the loss incurred when the machine places a ...
... functions . Central to the decision - theoretic treatment is the specifi- cation of a loss function , λ ( ij ) . Here λ ( ij ) is a function defined for i = 1 , R and j R and represents the loss incurred when the machine places a ...
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 particularly simple ... functions can be 46 PARAMETRIC TRAINING METHODS A special loss function,
... loss function We have shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple ... functions can be 46 PARAMETRIC TRAINING METHODS A special loss function,
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |