Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 38
... hypersphere , where W is the center of the hyper- sphere and a is its radius . Expanding the above equation yields Þ ... hypersphere Þ ( 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 Þ ... hypersphere Þ ( N , d ) = L ( N , d + 1 ) ( 2.41 ) The above expression assumes , of course , that the points in X ...
Sivu 40
... Hypersphere 2 ( d + 2 ) General quadric surface ( d + 1 ) ( d + 2 ) rth - order polynomial surface 2 ( d + r ) 2.17 Bibliographical and historical remarks A detailed treatment of the properties of hyperplane decision sur- faces is ...
... Hypersphere 2 ( d + 2 ) General quadric surface ( d + 1 ) ( d + 2 ) rth - order polynomial surface 2 ( d + r ) 2.17 Bibliographical and historical remarks A detailed treatment of the properties of hyperplane decision sur- faces is ...
Sivu 92
... hypersphere S ( W ) , centered at W and with some radius ( W ) . But the preceding statement is true for all W in W. Therefore , We must converge to one of the points defined by the joint intersection of all hyperspheres S ( W ) for all ...
... hypersphere S ( W ) , centered at W and with some radius ( W ) . But the preceding statement is true for all W in W. Therefore , We must converge to one of the points defined by the joint intersection of all hyperspheres S ( W ) for all ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 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,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |