Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 50
Sivu ix
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
Sivu 16
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
Sivu 30
... linear machine . 2.11 functions 9 We noted in Sec . 2.10 that a quadric discriminant function can be con- sidered to be a linear function of the components of a vector F. If we examine Eq ... DISCRIMINANT FUNCTIONS 11 The utility functions,
... linear machine . 2.11 functions 9 We noted in Sec . 2.10 that a quadric discriminant function can be con- sidered to be a linear function of the components of a vector F. If we examine Eq ... DISCRIMINANT FUNCTIONS 11 The utility functions,
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X 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 point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |