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 X xd Pattern +1 : w ; g ( x ) F wa Threshold element Summing device W 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 X xd Pattern +1 : w ; g ( x ) F wa Threshold element Summing device W 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 ) ...
... 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 ) ...
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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |