Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 8
... changes in the organization , structure , or parameter values of the parts of the machine , or it can occur before hardware construction by making these changes on a simulated machine using , for example , a digital computer . We shall ...
... changes in the organization , structure , or parameter values of the parts of the machine , or it can occur before hardware construction by making these changes on a simulated machine using , for example , a digital computer . We shall ...
Sivu 59
... changes as a result of being given a set of training patterns . We can see how it changes by first calculating an a posteriori density function for the mean vector M. ( By an a posteriori density function we mean the density function ...
... changes as a result of being given a set of training patterns . We can see how it changes by first calculating an a posteriori density function for the mean vector M. ( By an a posteriori density function we mean the density function ...
Sivu 66
... changes in the orientation and position of the hyperplane . A clearer picture of the precise effects of these weight adjust- ments is provided by an alternative geometric representation of the TLU , which will be discussed next . 4.2 ...
... changes in the orientation and position of the hyperplane . A clearer picture of the precise effects of these weight adjust- ments is provided by an alternative geometric representation of the TLU , which will be discussed next . 4.2 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |