Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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1 the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region Ri is the set of points which map into the ith category number . For convenience , we shall arbitrarily assume that patterns which ...
1 the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region Ri is the set of points which map into the ith category number . For convenience , we shall arbitrarily assume that patterns which ...
Sivu 67
Therefore , each region in weight space corresponds to a different linear dichotomy of the N patterns ... For any given linear dichotomy , the corre* If we count the number of regions in weight space formed by N augmented pattern ...
Therefore , each region in weight space corresponds to a different linear dichotomy of the N patterns ... For any given linear dichotomy , the corre* If we count the number of regions in weight space formed by N augmented pattern ...
Sivu 68
sponding region in weight space is called the solution region . It is a convex region containing all of the solution weight points W satisfying inequality ( 4.3 ) . These ideas are illustrated in Fig . 4 : 1 for a two - dimensional ...
sponding region in weight space is called the solution region . It is a convex region containing all of the solution weight points W satisfying inequality ( 4.3 ) . These ideas are illustrated in Fig . 4 : 1 for a two - dimensional ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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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 gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 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 |