Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 77
... Cell Assembly Theory of the Action of the Brain Using a Large Digital Computer , Trans . IRE on Info . Theory , vol . IT - 2 , no . 3 , pp . 80-93 , September , 1956 . 4 Farley , B. , and W. Clark : Simulation of Self - organizing ...
... Cell Assembly Theory of the Action of the Brain Using a Large Digital Computer , Trans . IRE on Info . Theory , vol . IT - 2 , no . 3 , pp . 80-93 , September , 1956 . 4 Farley , B. , and W. Clark : Simulation of Self - organizing ...
Sivu 108
... cells will merge into one cell . Some examples of nonredundant and redundant partitions are shown in Fig . 6-8 . Note that the partition shown in Fig . 6-7a is also nonredundant . A nonredundant partition is not necessarily one that ...
... cells will merge into one cell . Some examples of nonredundant and redundant partitions are shown in Fig . 6-8 . Note that the partition shown in Fig . 6-7a is also nonredundant . A nonredundant partition is not necessarily one that ...
Sivu 109
... cells when P planes are used in a nonredundant partition . Hence the resulting image- space vertices are linearly separable . Since two distinct finite subsets of pattern vectors can always be nonredundantly partitioned by a set of ...
... cells when P planes are used in a nonredundant partition . Hence the resulting image- space vertices are linearly separable . Since two distinct finite subsets of pattern vectors can always be nonredundantly partitioned by a set of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |