Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 20
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 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. 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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |