Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 20
... side and each member of X2 on the other side . Because the decision regions of a linear machine are convex 20 SOME IMPORTANT DISCRIMINANT FUNCTIONS Linear classifications of patterns,
... side and each member of X2 on the other side . Because the decision regions of a linear machine are convex 20 SOME IMPORTANT DISCRIMINANT FUNCTIONS Linear classifications of patterns,
Sivu 67
... side of the hyperplane , called the positive side , and those which produce a TLU response of minus one are on the other , or negative side . Note that the point representing the weight values w1 = 0 , w2 = 0 , O satisfies Eq . ( 4 · 2 ) ...
... side of the hyperplane , called the positive side , and those which produce a TLU response of minus one are on the other , or negative side . Note that the point representing the weight values w1 = 0 , w2 = 0 , O satisfies Eq . ( 4 · 2 ) ...
Sivu 69
... side of or on the pattern hyperplane corresponding to Y. This error can be rectified by moving W to the posi- tive side of the pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane ...
... side of or on the pattern hyperplane corresponding to Y. This error can be rectified by moving W to the posi- tive side of the pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane ...
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 g₁(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 second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns 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 |