Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 98
... committee of three TLUS in such a Y1 W2 ས་ Y 2 3 FIGURE 6.3 A commmittee of weight vectors way that the consensus or majority of the TLU responses is correct for each pattern . The consensus can be polled by an additional vote - taking TLU ...
... committee of three TLUS in such a Y1 W2 ས་ Y 2 3 FIGURE 6.3 A commmittee of weight vectors way that the consensus or majority of the TLU responses is correct for each pattern . The consensus can be polled by an additional vote - taking TLU ...
Sivu 99
... committee TLUS ( first layer ) Response Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the ...
... committee TLUS ( first layer ) Response Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the ...
Sivu 103
... committee weight vectors . At this stage the training process terminates . 1 3 This example can also be used to ... TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
... committee weight vectors . At this stage the training process terminates . 1 3 This example can also be used to ... TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
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 |