Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 43
Sivu 9
... training . The training process proceeds as follows : a large number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired classifications ...
... training . The training process proceeds as follows : a large number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired classifications ...
Sivu 69
... training set is called an iteration . Before training begins , the TLU weights may be preset to any con- venient values or they may be set to values selected SOME NONPARAMETRIC TRAINING METHODS FOR MACHINES 69 TLU training procedures,
... training set is called an iteration . Before training begins , the TLU weights may be preset to any con- venient values or they may be set to values selected SOME NONPARAMETRIC TRAINING METHODS FOR MACHINES 69 TLU training procedures,
Sivu 89
... set Z of higher - dimensional vectors from the training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD dimensions of Z as being split into R blocks of D dimensions each . Each D - dimensional ...
... set Z of higher - dimensional vectors from the training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD dimensions of Z as being split into R blocks of D dimensions each . Each D - dimensional ...
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 |