Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 56
We see that this case leads to linear discriminant functions and thus to linear machines . The first d weights employed by the ith discriminator are given by the values of the components of the transformed mean ...
We see that this case leads to linear discriminant functions and thus to linear machines . The first d weights employed by the ith discriminator are given by the values of the components of the transformed mean ...
Sivu 104
For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUS , we can say that it trans- forms the vertices ...
For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUS , we can say that it trans- forms the vertices ...
Sivu 121
Thus each typical pattern for a given category might be thought of as a " mode " of the probability- density function for that category . We use the word mode here to denote the location of a local maximum in the probability - density ...
Thus each typical pattern for a given category might be thought of as a " mode " of the probability- density function for that category . We use the word mode here to denote the location of a local maximum in the probability - density ...
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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 |