Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 36
Sivu 67
... hyperplane in weight space defined by Eq . ( 4 · 2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU ...
... hyperplane in weight space defined by Eq . ( 4 · 2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU ...
Sivu 68
... pattern hyperplanes ( lines ) indicate the positive side of each hyperplane . The A solution weight point W , Solution region 3 w Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled ...
... pattern hyperplanes ( lines ) indicate the positive side of each hyperplane . The A solution weight point W , Solution region 3 w Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled ...
Sivu 69
... pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y. W = 0 ) . That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error ...
... pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y. W = 0 ) . That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error ...
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 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 |