Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 77
... Cornell University . 11 Proof that these training procedures will either terminate or con- verge are given in ... Cornell Aeronautical Laboratory Report 85-460-1 , January , 1957 . 6 : " Principles of Neurodynamics : Perceptrons and the ...
... Cornell University . 11 Proof that these training procedures will either terminate or con- verge are given in ... Cornell Aeronautical Laboratory Report 85-460-1 , January , 1957 . 6 : " Principles of Neurodynamics : Perceptrons and the ...
Sivu 78
... Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B ... Cornell Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . CHAPTER 5 TRAINING ...
... Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B ... Cornell Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . CHAPTER 5 TRAINING ...
Sivu 93
... Cornell University . Our proof is a version of Kesler's as it was related to the author during discussions in July ... Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . 2 Joseph , R. D .: Contributions ...
... Cornell University . Our proof is a version of Kesler's as it was related to the author during discussions in July ... Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . 2 Joseph , R. D .: Contributions ...
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 |