Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 99
... committee machine with a fixed vote - taking TLU . x X Pattern = +1 d + 1 P committee TLUS ( first layer ) Response Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines ...
... committee machine with a fixed vote - taking TLU . x X Pattern = +1 d + 1 P committee TLUS ( first layer ) Response Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines ...
Sivu 100
... committee TLUS have negative responses . If the responses of at least 1⁄2 ( N + 1 ) of these negatively responding TLUS were changed from -1 to +1 , then the majority of the committee TLUS would have posi- tive responses , and the machine ...
... committee TLUS have negative responses . If the responses of at least 1⁄2 ( N + 1 ) of these negatively responding TLUS were changed from -1 to +1 , then the majority of the committee TLUS would have posi- tive responses , and the machine ...
Sivu 113
... committee machine was first proposed by Ridgway.5 Note that the committee machine and the a perceptron have a similar structure ; they are both two - layer ma- chines . Each has one layer of trainable TLUs ; they differ only in which ...
... committee machine was first proposed by Ridgway.5 Note that the committee machine and the a perceptron have a similar structure ; they are both two - layer ma- chines . Each has one layer of trainable TLUs ; they differ only in which ...
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 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 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 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 |