Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
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 57
... set of patterns belonging to a single category is a hyper- spherical cluster and each category is a priori equally probable . Then Eq . ( 3.33 ) could be written as gi ( X ) = X. M ... TRAINING METHODS 57 Training with normal pattern sets,
... set of patterns belonging to a single category is a hyper- spherical cluster and each category is a priori equally probable . Then Eq . ( 3.33 ) could be written as gi ( X ) = X. M ... TRAINING METHODS 57 Training with normal pattern sets,
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |