Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 37
Since any linear dichotomy of 5 in ♧ space corresponds to a dichotomy of X in the pattern space , ( N , d ) is equal to L ( N , M ) , which is the number of linear dichotomies of the set of N points in space .
Since any linear dichotomy of 5 in ♧ space corresponds to a dichotomy of X in the pattern space , ( N , d ) is equal to L ( N , M ) , which is the number of linear dichotomies of the set of N points in space .
Sivu 67
Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional , augmented patterns Y1 and Y2 . Each element of y1 and Y2 is obtained by augmenting the patterns in X1 and X2 , respectively .
Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional , augmented patterns Y1 and Y2 . Each element of y1 and Y2 is obtained by augmenting the patterns in X1 and X2 , respectively .
Sivu 89
... Sŵr a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of Sy is a vector Z in Sz . We determine Z as follows : Sup- pose that Ŷ ...
... Sŵr a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of Sy is a vector Z in Sz . We determine Z as follows : Sup- pose that Ŷ ...
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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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |