Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 28
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. Mi - 12M , M ; • i = 1 , R • • 9 ( 3.35 ) In Eq . ( 3.35 ) we ...
... 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. Mi - 12M , M ; • i = 1 , R • • 9 ( 3.35 ) In Eq . ( 3.35 ) we ...
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 , Y2 , . . . , YR which are linearly separable . The subset y ; con- Yi 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 , Y2 , . . . , YR which are linearly separable . The subset y ; con- Yi 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. j 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. j 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 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 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 |