Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 52
... pattern points in a two - dimensional space . Such patterns will be called bivariate normal patterns . They will ... prototype pattern for that category . As the number of sample points in a cluster increases , the coordinates of the ...
... pattern points in a two - dimensional space . Such patterns will be called bivariate normal patterns . They will ... prototype pattern for that category . As the number of sample points in a cluster increases , the coordinates of the ...
Sivu 116
... prototype " or typical pattern around which all other patterns in the category cluster . We shall say in this case that there is more than one mode per category . Suppose that there are L ; prototype patterns for the ith category and ...
... prototype " or typical pattern around which all other patterns in the category cluster . We shall say in this case that there is more than one mode per category . Suppose that there are L ; prototype patterns for the ith category and ...
Sivu 136
... Pattern hyperplanes , 67 negative side of , 67 positive side of , 67 Pattern point , 5 Pattern , prototype , 18 , 52 Pattern sets , linearly separable , 20 linearly contained . 82 Pattern space , 5 Pattern vector , 5 augmented , 66 Patterns ...
... Pattern hyperplanes , 67 negative side of , 67 positive side of , 67 Pattern point , 5 Pattern , prototype , 18 , 52 Pattern sets , linearly separable , 20 linearly contained . 82 Pattern space , 5 Pattern vector , 5 augmented , 66 Patterns ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |