Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 75
... belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum ... category i . We desire to train the linear machine by adjusting its weight vectors so that it responds correctly to every ...
... belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum ... category i . We desire to train the linear machine by adjusting its weight vectors so that it responds correctly to every ...
Sivu 116
... category . Suppose that there are Li 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 Li 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. 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 having the ...
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. 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 having the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks 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 space Stanford step subsidiary discriminant 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 |