Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 36
... contain all the points of Z. We shall assume that the points of Z are in general position , meaning , in this case , that no ( K - 2 ) -dimensional hyperplane contains all of them . The set Z The set consists of three points on a line ...
... contain all the points of Z. We shall assume that the points of Z are in general position , meaning , in this case , that no ( K - 2 ) -dimensional hyperplane contains all of them . The set Z The set consists of three points on a line ...
Sivu 102
... contains Y2 and that Y2 contains - Y1 and —Y ̧ . ) k 1 The successive weight vectors are indicated by points and the ap- propriate labels W , adjacent to them . The shaded regions indicate those regions that must each contain one of the ...
... contains Y2 and that Y2 contains - Y1 and —Y ̧ . ) k 1 The successive weight vectors are indicated by points and the ap- propriate labels W , adjacent to them . The shaded regions indicate those regions that must each contain one of the ...
Sivu 105
... contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to a vertex in 91 space . Thus , the four ...
... contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to a vertex in 91 space . Thus , the four ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |