Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 80
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , • • 9 Yk ... 1 . Each Y in Sy is a member of y . ( 5.2 ) k 2 ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , • • 9 Yk ... 1 . Each Y in Sy is a member of y . ( 5.2 ) k 2 ...
Sivu 82
... sequence on the adjusted pattern set y ' . Each element of the sequence Sy , is obtained from the corresponding sequence Sy by the relations Yk ' Y1 = Yk YK = Yk if Yk € Yı if Yk € Y2 ( 5.7 ) The fixed - increment error - correction ...
... sequence on the adjusted pattern set y ' . Each element of the sequence Sy , is obtained from the corresponding sequence Sy by the relations Yk ' Y1 = Yk YK = Yk if Yk € Yı if Yk € Y2 ( 5.7 ) The fixed - increment error - correction ...
Sivu 93
... sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with WK + 1 . They also show ...
... sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with WK + 1 . They also show ...
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 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 |