Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 6
... plane . Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS Discriminant functions,
... plane . Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS Discriminant functions,
Sivu 71
... plane . For λ = 0 , the weight point is not moved at all ; for λ = 1 , the weight point is moved to the pattern ... plane ( e.g. , across it ) , and then the next pattern in the set is examined . The process continues until a solution ...
... plane . For λ = 0 , the weight point is not moved at all ; for λ = 1 , the weight point is moved to the pattern ... plane ( e.g. , across it ) , and then the next pattern in the set is examined . The process continues until a solution ...
Sivu 107
... plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine is a ...
... plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine is a ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 |