Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 19
Sivu 68
... plane divides one of the original R ( N 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N − 1 , D − 1 ) new regions . This fact gives us the relation - R ( N ...
... plane divides one of the original R ( N 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N − 1 , D − 1 ) new regions . This fact gives us the relation - R ( N ...
Sivu 73
... plane 0,0,0 0,1,0 1,1,0 O Patterns requiring -1 response Patterns requiring +1 response FIGURE 4.3 A plane which correctly partitions eight three - dimensional patterns 3 -2 Y -3 F Response Threshold element Y4 = +1 Augmented pattern ...
... plane 0,0,0 0,1,0 1,1,0 O Patterns requiring -1 response Patterns requiring +1 response FIGURE 4.3 A plane which correctly partitions eight three - dimensional patterns 3 -2 Y -3 F Response Threshold element Y4 = +1 Augmented pattern ...
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 |
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 |