Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 29
Sivu 28
... positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The ...
... positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The ...
Sivu 101
... positive ) dot products with Yk . If the weight vector W ( ) is among this set of 2 ( | N | + 1 ) weight vectors , it is adjusted by the rule k W , ( k + 1 ) = W ̧ ( k ) + c ; ( k ) Yk ( 6 · 7 ) where c ) is the correction increment ...
... positive ) dot products with Yk . If the weight vector W ( ) is among this set of 2 ( | N | + 1 ) weight vectors , it is adjusted by the rule k W , ( k + 1 ) = W ̧ ( k ) + c ; ( k ) Yk ( 6 · 7 ) where c ) is the correction increment ...
Sivu 127
... positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column vector , and C is a scalar . The first term on the right - hand ...
... positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column vector , and C is a scalar . The first term on the right - hand ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |