Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... function for a layered machine , 109 6.8 Bibliographical and historical remarks , 113 References , 113 7 PIECEWISE ... quadratic form into positive and negative parts , 127 A.2 Implementation , 128 A.3 Transformation of normal patterns ...
... function for a layered machine , 109 6.8 Bibliographical and historical remarks , 113 References , 113 7 PIECEWISE ... quadratic form into positive and negative parts , 127 A.2 Implementation , 128 A.3 Transformation of normal patterns ...
Sivu 55
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( X | i ) that apply . That is , we know R normal density ... function , the opti- mum classifier uses the discriminant functions given by = gi ( X ) ...
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( X | i ) that apply . That is , we know R normal density ... function , the opti- mum classifier uses the discriminant functions given by = gi ( X ) ...
Sivu 136
... functions , 44 Probability functions , 43 Probabilistic pattern sets , 43 Prototype pattern , 18 , 52 Quadratic form , 27 , 56 , 127 eigenvalues of , 27 , 128 Quadric decision surfaces , 28 , 38 equation of , 28 names of , 28 Quadric ...
... functions , 44 Probability functions , 43 Probabilistic pattern sets , 43 Prototype pattern , 18 , 52 Quadratic form , 27 , 56 , 127 eigenvalues of , 27 , 128 Quadric decision surfaces , 28 , 38 equation of , 28 names of , 28 Quadric ...
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,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |