Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 24
Let us define the Euclidean distance d ( X , P , ) from an arbi- trary point X to the point set P ; by d ( X , Pi ) = i min j = 1 , ... , Li | X — P , ( ~ | ( 2 · 16 ) That is , the distance between X and P , is the smallest of the ...
Let us define the Euclidean distance d ( X , P , ) from an arbi- trary point X to the point set P ; by d ( X , Pi ) = i min j = 1 , ... , Li | X — P , ( ~ | ( 2 · 16 ) That is , the distance between X and P , is the smallest of the ...
Sivu 47
As in Chapter 1 , we define the discriminant function , g ( X ) = 91 ( X ) 91 ( X ) — g2 ( X ) . If g ( X ) > 0 , the machine places X in category 1 ; if g ( X ) < 0 , the machine places X in category 2. From Eq . ( 3.7b ) we can derive ...
As in Chapter 1 , we define the discriminant function , g ( X ) = 91 ( X ) 91 ( X ) — g2 ( X ) . If g ( X ) > 0 , the machine places X in category 1 ; if g ( X ) < 0 , the machine places X in category 2. From Eq . ( 3.7b ) we can derive ...
Sivu 53
The notation used in Eq . ( 3-20 ) to describe the normal distribution can be made more compact if we define and use the following matrices . Let the pattern vector X be a column vector ( a 2 X 1 matrix ) with compo- Category 3 Category ...
The notation used in Eq . ( 3-20 ) to describe the normal distribution can be made more compact if we define and use the following matrices . Let the pattern vector X be a column vector ( a 2 X 1 matrix ) with compo- Category 3 Category ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |
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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |