Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 57
... belonging to each of the R categories is available . It consists of R subsets denoted by X1 , X2 , ... , XR , where X is the training subset of all patterns belonging to category i . These subsets can be used to estimate Σ ; and M ...
... belonging to each of the R categories is available . It consists of R subsets denoted by X1 , X2 , ... , XR , where X is the training subset of all patterns belonging to category i . These subsets can be used to estimate Σ ; and M ...
Sivu 116
... category . Suppose that there are L ; prototype patterns for the ith category and that all patterns belonging to category i are close to one of these prototypes . Then , a PWL machine with L ; linear discrimi- nators in the Lith bank ...
... category . Suppose that there are L ; prototype patterns for the ith category and that all patterns belonging to category i are close to one of these prototypes . Then , a PWL machine with L ; linear discrimi- nators in the Lith bank ...
Sivu 121
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. ... 9 Then , given these modes , one reasonable way to classify some arbi- trary pattern X is to measure its distance to each of the modes and place it in that category ...
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. ... 9 Then , given these modes , one reasonable way to classify some arbi- trary pattern X is to measure its distance to each of the modes and place it in that category ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |