Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 9
Sivu 53
... column vector ( a 2 × 1 matrix ) with compo- Category 2 x2 Category 3 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x and x2 . Similarly , let the mean vector M be a column vector with components m1 and m2 . We ...
... column vector ( a 2 × 1 matrix ) with compo- Category 2 x2 Category 3 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x and x2 . Similarly , let the mean vector M be a column vector with components m1 and m2 . We ...
Sivu 54
... column vector representing the pattern . is ad X1 column vector . It has the property of being equal to the expected value of X ( i.e. , M = E [ X ] ) and is therefore called the mean vector . 012 σij • Old Odd is a symmetric , positive ...
... column vector representing the pattern . is ad X1 column vector . It has the property of being equal to the expected value of X ( i.e. , M = E [ X ] ) and is therefore called the mean vector . 012 σij • Old Odd is a symmetric , positive ...
Sivu 108
... columns of M be a column of zeros . Let us delete this column from M to form a square PX P matrix M. Each column of ✩ is a vertex belonging to either g1 ( 1 ) or §2 ( 1 ) . * In the following proof we do not make use of the fact that ...
... columns of M be a column of zeros . Let us delete this column from M to form a square PX P matrix M. Each column of ✩ is a vertex belonging to either g1 ( 1 ) or §2 ( 1 ) . * In the following proof we do not make use of the fact that ...
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 |