Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 8
Sivu 48
... optimum discriminant function is W1 xd : ω ; g ( x ) F : d Threshold element Summing device Pattern wd +1 +1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this ...
... optimum discriminant function is W1 xd : ω ; g ( x ) F : d Threshold element Summing device Pattern wd +1 +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 136
... Optimum classifier , for binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 ...
... Optimum classifier , for binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described 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 hyperplane pattern space pattern vector pattern-classifying 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |