Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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It is known a priori that the pattern points in category 1 tend to cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 .
It is known a priori that the pattern points in category 1 tend to cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 .
Sivu 52
Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging to category 2 , etc. For each category a cluster of ...
Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging to category 2 , etc. For each category a cluster of ...
Sivu 122
It seems reasonable to assume that the k closest training patterns to a given pattern X will often include a predominant number of patterns from the cluster surrounding the closest mode . Thus the " closest - mode " method just ...
It seems reasonable to assume that the k closest training patterns to a given pattern X will often include a predominant number of patterns from the cluster surrounding the closest mode . Thus the " closest - mode " method just ...
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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 |