Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 4
1 : 3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical ...
1 : 3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical ...
Sivu 8
In the following sections we shall discuss a class of methods by which this selection might be made . > 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways .
In the following sections we shall discuss a class of methods by which this selection might be made . > 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways .
Sivu 15
... PROPERTIES AND THEIR IMPLEMENTATIONS 2.1 Families of discriminant functions a The task of selecting a discriminant function ... machine is simplified by first limiting the class of functions from which the selection is to be made .
... PROPERTIES AND THEIR IMPLEMENTATIONS 2.1 Families of discriminant functions a The task of selecting a discriminant function ... machine is simplified by first limiting the class of functions from which the selection is to be made .
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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 |