Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 19
Sivu 75
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R ... METHODS FOR MACHINES 75 An error-correction training procedure for R > 2,
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R ... METHODS FOR MACHINES 75 An error-correction training procedure for R > 2,
Sivu 81
... error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of c is taken to be the smallest integer for which cY Yk > Wk . Yk . With this pro ...
... error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of c is taken to be the smallest integer for which cY Yk > Wk . Yk . With this pro ...
Sivu 119
... error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should ... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision ...
... error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should ... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision ...
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 |