Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 18
An equivalent classification is obtained by comparing the squared distances X –
P : / ? , i = 1 , . . . , R . Squaring both sides ... Pi ( 2 - 4 ) The minimum - distance
classification can be effected by comparing the expressions X · Pi – YP ; Pi for i =
1 ...
An equivalent classification is obtained by comparing the squared distances X –
P : / ? , i = 1 , . . . , R . Squaring both sides ... Pi ( 2 - 4 ) The minimum - distance
classification can be effected by comparing the expressions X · Pi – YP ; Pi for i =
1 ...
Sivu 24
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the
point set Pi by d ( X , P : ) = min j = 1 , . . . , L X ... ( 0 ) | ( 2 - 16 ) That is , the
distance between X and Pi is the smallest of the distances between X and each
point in Pi .
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the
point set Pi by d ( X , P : ) = min j = 1 , . . . , L X ... ( 0 ) | ( 2 - 16 ) That is , the
distance between X and Pi is the smallest of the distances between X and each
point in Pi .
Sivu 71
In one case , c is a fixed constant so that the distance moved toward a particular
pattern hyperplane is always the same . This fixed distance may or may not be
sufficient to cross the pattern hyperplane and thus correct the error . In another ...
In one case , c is a fixed constant so that the distance moved toward a particular
pattern hyperplane is always the same . This fixed distance may or may not be
sufficient to cross the pattern hyperplane and thus correct the error . In another ...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 reduced regions respect response rule sample mean selection separable shown side solution space specific 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 |