Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 24
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the point set Pi by • . > 1 d ( X , 0 ) min j = 1 , ... , L . X - P. ) ( 2:16 ) That is , the distance between X and Pi is the smallest of the distances ...
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the point set Pi by • . > 1 d ( X , 0 ) min j = 1 , ... , L . X - P. ) ( 2:16 ) That is , the distance between X and Pi is the smallest of the distances ...
Sivu 47
As in Chapter 1 , we define the discriminant function , g ( x ) = 91 ( X ) – 92 ( 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 . ( 3.76 ) we can derive g ( X ) ...
As in Chapter 1 , we define the discriminant function , g ( x ) = 91 ( X ) – 92 ( 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 . ( 3.76 ) we can derive g ( X ) ...
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 compoa 2 Category 3 ...
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 compoa 2 Category 3 ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric regions respect response rule sample mean selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors 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 |