Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 48
... optimum discriminant function is W1 : wi g ( x ) F Threshold element w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this ...
... optimum discriminant function is W1 : wi g ( x ) F Threshold element w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this ...
Sivu 55
... optimum classifier for normal patterns " We are now ready to derive the optimum classifier for normal patterns . We shall temporarily assume that for each category i , where i = 1 , R , we know the a priori probability p ( i ) and the ...
... optimum classifier for normal patterns " We are now ready to derive the optimum classifier for normal patterns . We shall temporarily assume that for each category i , where i = 1 , R , we know the a priori probability p ( i ) and the ...
Sivu 119
... optimum decision surface will not perfectly separate all the members of the two training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 ...
... optimum decision surface will not perfectly separate all the members of the two training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 ...
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 |