Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 37
... illustrate the use of the above expression : 1. ( X ) is a special quadric function of the form - Ž ( X ) = | X – W2 X - W2 a2 ( 2 · 39 ) Here , ( X ) = O defines a hypersphere SOME IMPORTANT DISCRIMINANT FUNCTIONS 37.
... illustrate the use of the above expression : 1. ( X ) is a special quadric function of the form - Ž ( X ) = | X – W2 X - W2 a2 ( 2 · 39 ) Here , ( X ) = O defines a hypersphere SOME IMPORTANT DISCRIMINANT FUNCTIONS 37.
Sivu 38
... hypersphere , where W is the center of the hyper- sphere and a is its radius . Expanding the above equation yields Ž ... hypersphere Ž ( N , d ) = L ( N , d + 1 ) ( 2 · 41 ) The above expression assumes , of course , that the points in X ...
... hypersphere , where W is the center of the hyper- sphere and a is its radius . Expanding the above equation yields Ž ... hypersphere Ž ( N , d ) = L ( N , d + 1 ) ( 2 · 41 ) The above expression assumes , of course , that the points in X ...
Sivu 134
... hypersphere , 38 for functions , 30 for a quadric function , 38 Dichotomizer , 8 linear , 21 Dichotomy , linear , 20 , 67 Discriminant , 7 Discriminant functions , 6 , 15 , 47 adjustment of , 8 families of , 15 implementation of linear ...
... hypersphere , 38 for functions , 30 for a quadric function , 38 Dichotomizer , 8 linear , 21 Dichotomy , linear , 20 , 67 Discriminant , 7 Discriminant functions , 6 , 15 , 47 adjustment of , 8 families of , 15 implementation of linear ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 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,Lįszló Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |