Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 39
Sivu 103
... weight vectors . At this stage the training process terminates . 1 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W21 ) were many ...
... weight vectors . At this stage the training process terminates . 1 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W21 ) were many ...
Sivu 117
... weight vectors , by transferring weight vectors among the banks , or both . The second aspect of training might be avoided by ensuring a surplus of subsidiary discriminators in each bank , although such an exuberant use of subsidiary ...
... weight vectors , by transferring weight vectors among the banks , or both . The second aspect of training might be avoided by ensuring a surplus of subsidiary discriminators in each bank , although such an exuberant use of subsidiary ...
Sivu 122
... weight vectors * w ) for i = 1 , . . . , R and j . = 1 , . L. Thus there are R banks of weight vectors , and the ith bank has Li members . Any training method which adjusts the weight vectors in each bank so that each weight vector ...
... weight vectors * w ) for i = 1 , . . . , R and j . = 1 , . L. Thus there are R banks of weight vectors , and the ith bank has Li members . Any training method which adjusts the weight vectors in each bank so that each weight vector ...
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 |