Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 15
Sivu 77
... Computer , Trans . IRE on Info . Theory , vol . IT - 2 , no . 3 , pp . 80-93 , September , 1956 . 4 Farley , B. , and W. Clark : Simulation of Self - organizing Systems by Digital Computer , Trans . IRE on Info . Theory , PGIT - 4 , pp ...
... Computer , Trans . IRE on Info . Theory , vol . IT - 2 , no . 3 , pp . 80-93 , September , 1956 . 4 Farley , B. , and W. Clark : Simulation of Self - organizing Systems by Digital Computer , Trans . IRE on Info . Theory , PGIT - 4 , pp ...
Sivu 126
... Computers , vol . EC - 12 , no . 2 , pp . 137-141 , April , 1963 . 6 Jakowatz , C. V. , R. L. Shuey , and G. M. White ... Computer Pattern Recognition Techniques : Electrocardiographic Diagnosis , Comm . of the ACM , vol . 5 , pp . 527 ...
... Computers , vol . EC - 12 , no . 2 , pp . 137-141 , April , 1963 . 6 Jakowatz , C. V. , R. L. Shuey , and G. M. White ... Computer Pattern Recognition Techniques : Electrocardiographic Diagnosis , Comm . of the ACM , vol . 5 , pp . 527 ...
Sivu
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Math & Computer Sciences Library C.2 Q 335 .N5 Learning machines Stanford University Libraries 3 6105 031 475 358 9335 · N5 . Cop . 2 Math & Computer Sciences Library ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Math & Computer Sciences Library C.2 Q 335 .N5 Learning machines Stanford University Libraries 3 6105 031 475 358 9335 · N5 . Cop . 2 Math & Computer Sciences Library ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |