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 x2 Pattern P committee TLUS * d + 1 = +1 d + 1 Response Vote - taking TLU ( second layer ) ( first layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee ...
... committee machine with a fixed vote - taking TLU . X x2 Pattern P committee TLUS * d + 1 = +1 d + 1 Response Vote - taking TLU ( second layer ) ( first layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee ...
Sivu 100
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUS in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUS in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
Sivu 103
... committee weight vectors . At this stage the training process terminates . 1 3 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W2 ...
... committee weight vectors . At this stage the training process terminates . 1 3 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W2 ...
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 |