Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 29
Sivu vii
... properties of various discriminant functions or to find methods for their selection or adjustment . The following topics are given special treatment : Parametric and nonparametric training methods . The decision- theoretic approach is ...
... properties of various discriminant functions or to find methods for their selection or adjustment . The following topics are given special treatment : Parametric and nonparametric training methods . The decision- theoretic approach is ...
Sivu 11
... properties of some in detail , and present block diagrams to suggest the manner in which they might be employed . Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these ...
... properties of some in detail , and present block diagrams to suggest the manner in which they might be employed . Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these ...
Sivu 16
... properties , leaving the subject of training to later chapters . One of the simplest is the family of linear functions to which we now turn . 2.2 Linear discriminant functions Let us consider first the family of discriminant functions ...
... properties , leaving the subject of training to later chapters . One of the simplest is the family of linear functions to which we now turn . 2.2 Linear discriminant functions Let us consider first the family of discriminant functions ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |