Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 52
... pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging to category 2 , etc. For each category a cluster of pattern points exists that is more ...
... pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging to category 2 , etc. For each category a cluster of pattern points exists that is more ...
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 i gi ( X ) = X. M1⁄2M ; 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 i gi ( X ) = X. M1⁄2M ; 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- 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- tains all ...
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 step subsidiary discriminant Suppose terns 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 |