Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 20
Sivu 4
... consist of a finite set of real numbers . 1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting ...
... consist of a finite set of real numbers . 1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting ...
Sivu 98
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUS to have ...
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUS to have ...
Sivu 115
... consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary discriminators ...
... consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary discriminators ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
4 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges gi(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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying 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 |