Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 98
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUS to have ...
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUS to have ...
Sivu 111
... consist of those U vectors for which H ( U ) +1 . Let H2 be a matrix whose rows consist of the remaining U vectors . If there are L U vec- tors for which H ( U ) +1 , then H1 will be an L X P matrix and H2 will be a ( 2PL ) X P matrix ...
... consist of those U vectors for which H ( U ) +1 . Let H2 be a matrix whose rows consist of the remaining U vectors . If there are L U vec- tors for which H ( U ) +1 , then H1 will be an L X P matrix and H2 will be a ( 2PL ) X P matrix ...
Sivu 128
... consisting of the first p1 columns of T , and let T2 be a d × p2 matrix consisting of the next p2 columns of T. Be- cause T is orthogonal we can write Eq . ( A · 3 ) as follows : A = TAT ( A · 5 ) In terms of the matrices just defined ...
... consisting of the first p1 columns of T , and let T2 be a d × p2 matrix consisting of the next p2 columns of T. Be- cause T is orthogonal we can write Eq . ( A · 3 ) as follows : A = TAT ( A · 5 ) In terms of the matrices just defined ...
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 |