Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 33
Sivu 55
... pattern space . A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for ...
... pattern space . A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for ...
Sivu 57
... patterns belonging to a single category is a hyper- spherical cluster and each category is a priori equally probable . Then Eq . ( 3.33 ) could be written ... patterns in the PARAMETRIC TRAINING METHODS 57 Training with normal pattern sets,
... patterns belonging to a single category is a hyper- spherical cluster and each category is a priori equally probable . Then Eq . ( 3.33 ) could be written ... patterns in the PARAMETRIC TRAINING METHODS 57 Training with normal pattern sets,
Sivu 62
... normal distribution is well treated in a book by Anderson . Equation ( 3 · 31 ) for the quadric discriminant functions , opti- mum for normal patterns , has been previously derived by Anderson and others . A similar derivation motivated ...
... normal distribution is well treated in a book by Anderson . Equation ( 3 · 31 ) for the quadric discriminant functions , opti- mum for normal patterns , has been previously derived by Anderson and others . A similar derivation motivated ...
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 |