Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 9
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
They will tend to be grouped in an ellipsoidal cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ...
They will tend to be grouped in an ellipsoidal cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ...
Sivu 133
... of a quadric surface , 40 Center of gravity of a cluster , 53 Charnes , 92 , 93 Clark , 76 , 77 Classification , linear ... 122 Cluster point , 9 , 43 Clustering transformations , 12 , 131 Clusters , ellipsoidal , 52 , 131 center of ...
... of a quadric surface , 40 Center of gravity of a cluster , 53 Charnes , 92 , 93 Clark , 76 , 77 Classification , linear ... 122 Cluster point , 9 , 43 Clustering transformations , 12 , 131 Clusters , ellipsoidal , 52 , 131 center of ...
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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 |