Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 42
Sivu 103
... weight vectors . At this stage the training process terminates . 1 3 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 W2 ( 1 ) were ...
... weight vectors . At this stage the training process terminates . 1 3 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 W2 ( 1 ) were ...
Sivu 122
... weight vectors * w , for i = 1 , . . . , R and j = 1 , . . . , w ; ) L. Thus there are R banks of weight vectors , and the ith bank has L 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 , . . . , w ; ) L. Thus there are R banks of weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector ...
Sivu 124
... weight vectors settle down at locations which are centers of gravity of subsets of the patterns . The above training ... weight vector is to be adjusted . Among the modifications which might be suggested to alleviate these difficulties ...
... weight vectors settle down at locations which are centers of gravity of subsets of the patterns . The above training ... weight vector is to be adjusted . Among the modifications which might be suggested to alleviate these difficulties ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 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 step subsidiary discriminant Suppose terns 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 |