Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 66
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights wi , w2 , wa , wa + 1 . Training a TLU to dichotomize ... Weight space,
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights wi , w2 , wa , wa + 1 . Training a TLU to dichotomize ... Weight space,
Sivu 67
... 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 response of one are ...
... 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 response of one are ...
Sivu 68
... space is called the solution region . It is a con- vex region containing all of the solution weight points W satisfying inequality ( 4 · 3 ) . These ideas are illustrated in Fig . 4.1 for a two - dimensional weight space ( D = 2 ) . In ...
... space is called the solution region . It is a con- vex region containing all of the solution weight points W satisfying inequality ( 4 · 3 ) . These ideas are illustrated in Fig . 4.1 for a two - dimensional weight space ( D = 2 ) . In ...
Sisältö
Preface vii | 11 |
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 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 |