Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 36
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 ( ) 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 ( ) were ...
Sivu 122
... weight vectors * w , for i = 1 , ... , R and j 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 finally resides ...
... weight vectors * w , for i = 1 , ... , R and j 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 finally resides ...
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 belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |