Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 9
... parameters , some of whose values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for ...
... parameters , some of whose values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for ...
Sivu 43
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
Sivu 44
... parameters , whose values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 ... parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then ...
... parameters , whose values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 ... parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then ...
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 |