Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 57
... 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 ; i 11⁄2M ; M ; i = 1 , • R ( 3.35 ) In Eq . ( 3.35 ) we have ...
... 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 ; i 11⁄2M ; M ; i = 1 , • R ( 3.35 ) In Eq . ( 3.35 ) we have ...
Sivu 75
... patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and ... patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- Y ; tains all ...
... patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and ... patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- Y ; tains all ...
Sivu 121
... patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The ... belonging to cate- gory 1 , L2 belonging to category 2 , etc. Then , given these modes , one reasonable way to ...
... patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The ... belonging to cate- gory 1 , L2 belonging to category 2 , etc. Then , given these modes , one reasonable way to ...
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 |