Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 15
Sivu 7
... negative , X is placed in category 2. * The equation g ( X ) O gives the decision surface sepa- - = * Even in the case R > 2 the number of discriminant functions can be reduced from R to R 1 by selecting one of them , say g1 ( X ) , and ...
... negative , X is placed in category 2. * The equation g ( X ) O gives the decision surface sepa- - = * Even in the case R > 2 the number of discriminant functions can be reduced from R to R 1 by selecting one of them , say g1 ( X ) , and ...
Sivu 28
... negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and ...
... negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and ...
Sivu 100
... negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ( ) , W2 ( * ) , ... , and Wp ( ) . 1 2 In describing the rule for modifying the weight vectors we shall make use of the notation where Nk = Σ sgn ( W ...
... negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ( ) , W2 ( * ) , ... , and Wp ( ) . 1 2 In describing the rule for modifying the weight vectors we shall make use of the notation where Nk = Σ sgn ( W ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
6 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 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 |