Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 13
... U.S. Army Signal Research and Development Laboratory under Contract DA 36-039- SC - 78343 and continuation , 1962 and 1963 . CHAPTER 2 SOME IMPORTANT DISCRIMINANT FUNCTIONS : THEIR PROPERTIES AND TRAINABLE PATTERN CLASSIFIERS 13.
... U.S. Army Signal Research and Development Laboratory under Contract DA 36-039- SC - 78343 and continuation , 1962 and 1963 . CHAPTER 2 SOME IMPORTANT DISCRIMINANT FUNCTIONS : THEIR PROPERTIES AND TRAINABLE PATTERN CLASSIFIERS 13.
Sivu 41
... Development Center Technical Documentary Report RADC - TDR - 64-32 , February , 1964 . 8 : Geometrical and Statistical Properties of Linear Threshold Devices , Stanford Electronics Laboratories Technical Report 6107-1 , May , 1964 . 9 ...
... Development Center Technical Documentary Report RADC - TDR - 64-32 , February , 1964 . 8 : Geometrical and Statistical Properties of Linear Threshold Devices , Stanford Electronics Laboratories Technical Report 6107-1 , May , 1964 . 9 ...
Sivu 62
... Development , " vol . 1 , Rome Air Development Center Technical Report RADC - 7R - 60-70A , pp . 1-21 , April , 1960 . 3 Minsky , M .: Steps toward Artificial Intelligence , Proc . IRE , vol . 49 , no . 1 , p . 14 , January , 1961 . 4 ...
... Development , " vol . 1 , Rome Air Development Center Technical Report RADC - 7R - 60-70A , pp . 1-21 , April , 1960 . 3 Minsky , M .: Steps toward Artificial Intelligence , Proc . IRE , vol . 49 , no . 1 , p . 14 , January , 1961 . 4 ...
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 step subsidiary discriminant Suppose terns 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 |