Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 52
... origin . Contours of equal probability density ( z12-20122122 +222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 1012 When ...
... origin . Contours of equal probability density ( z12-20122122 +222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 1012 When ...
Sivu 67
... origin of weight space . " Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional ... origin of a D - dimensional space . Suppose we have N hyperplanes intersecting at a point ( the origin ) in a D ...
... origin of weight space . " Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional ... origin of a D - dimensional space . Suppose we have N hyperplanes intersecting at a point ( the origin ) in a D ...
Sivu 105
... Origin * 1 ( a ) Pattern space 3 * 2 1,4,5,8 3,7 TLU 3 ! Origin TLU 2 - * TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans ...
... Origin * 1 ( a ) Pattern space 3 * 2 1,4,5,8 3,7 TLU 3 ! Origin TLU 2 - * TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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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 |