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 Ž ( X ) = - X · X – 2X · W + W • W a2 ( 2 · 40 ) Therefore F is ( d + 1 ) -dimensional ; i.e. , M = d + 1. The first d ...
... hypersphere , where W is the center of the hyper- sphere and a is its radius . Expanding the above equation yields Ž ( X ) = - X · X – 2X · W + W • W a2 ( 2 · 40 ) Therefore F is ( d + 1 ) -dimensional ; i.e. , M = d + 1. The first d ...
Sivu 92
... hypersphere S ( W ) , centered at W and with some radius 1 ( 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 ...
... hypersphere S ( W ) , centered at W and with some radius 1 ( 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 ...
Sivu 134
... hypersphere , 38 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 , 17 ...
... hypersphere , 38 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 , 17 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,Lįszló Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |