Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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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
... ideas are illustrated in Fig . 4.1 for a two - dimensional weight space ( D = 2 ) . In this example , the small arrows attached to the pattern hyperplanes ( lines ) indicate the positive side of each hyperplane . The A solution weight ...
... ideas are illustrated in Fig . 4.1 for a two - dimensional weight space ( D = 2 ) . In this example , the small arrows attached to the pattern hyperplanes ( lines ) indicate the positive side of each hyperplane . The A solution weight ...
Sivu 69
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
Sisältö
Preface vii | 11 |
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 important 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 |