Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 6
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number ...
Sivu 28
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R ; and R ; share a common boundary , it is a section of the surface S , given ...
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R ; and R ; share a common boundary , it is a section of the surface S , given ...
Sivu 119
... decision surface which minimizes the probability of error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should be observed that if the two density func- tions overlap ...
... decision surface which minimizes the probability of error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should be observed that if the two density func- tions overlap ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ 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 |