Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 77
... Cornell University . Proof that these training procedures will either terminate or con- verge are given in Chapter 5 ... Cornell Aeronautical Laboratory Report 85-460-1 , January , 1957 . 6 - : " Principles of Neurodynamics : Perceptrons ...
... Cornell University . Proof that these training procedures will either terminate or con- verge are given in Chapter 5 ... Cornell Aeronautical Laboratory Report 85-460-1 , January , 1957 . 6 - : " Principles of Neurodynamics : Perceptrons ...
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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |