Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Let gi ( X ) , 32 ( X ) , . . . , gr ( X ) be :: 9R g 9 , ( x ) 9. ( X ) g , ( x ) Pa R ; * 2 R , FIGURE 1.3 Examples of discriminant functions for two - dimensional patterns > scalar and single - valued functions of the pattern X.
Let gi ( X ) , 32 ( X ) , . . . , gr ( X ) be :: 9R g 9 , ( x ) 9. ( X ) g , ( x ) Pa R ; * 2 R , FIGURE 1.3 Examples of discriminant functions for two - dimensional patterns > scalar and single - valued functions of the pattern X.
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It is clear that the discriminant functions in this case can be given by gi ( X ) = X · Pi - YP : Pi ; • for i 1 , R ( 2.5 ) We conclude that a minimum - distance classifier is a linear machine . Suppose that the components of Pi are ...
It is clear that the discriminant functions in this case can be given by gi ( X ) = X · Pi - YP : Pi ; • for i 1 , R ( 2.5 ) We conclude that a minimum - distance classifier is a linear machine . Suppose that the components of Pi are ...
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gi ( X ) max j = 1 , ... , Li { P : 6 ) . X - 12P : 6 ) . P , 0 ) } ( 2017 ) . . Note that { P / 60 ) .X – 12P , 00 ) . P : ( 1 ) will be a maximum for that P : 6 ) in Pi which is closest to X. Therefore , for any X , the largest of the ...
gi ( X ) max j = 1 , ... , Li { P : 6 ) . X - 12P : 6 ) . P , 0 ) } ( 2017 ) . . Note that { P / 60 ) .X – 12P , 00 ) . P : ( 1 ) will be a maximum for that P : 6 ) in Pi which is closest to X. Therefore , for any X , the largest of the ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 reduced regions respect response rule sample mean selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |