Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 26
Sivu 79
... theorem In this chapter we shall formally state and prove some theorems about the training procedures mentioned in Chapter 4. These theorems form the core of the theory of iterative nonparametric training methods , and their ...
... theorem In this chapter we shall formally state and prove some theorems about the training procedures mentioned in Chapter 4. These theorems form the core of the theory of iterative nonparametric training methods , and their ...
Sivu 88
... theorem . Theorem 5.2 Y2 , · • Let the training subsets Y1 , 2 , ... , YR be linearly separable . Let Sw , Sw ,, . . . , SwR be the weight - vector sequences generated by ... Theorem 5.2 is accomplished by reformulating 88 TRAINING THEOREMS.
... theorem . Theorem 5.2 Y2 , · • Let the training subsets Y1 , 2 , ... , YR be linearly separable . Let Sw , Sw ,, . . . , SwR be the weight - vector sequences generated by ... Theorem 5.2 is accomplished by reformulating 88 TRAINING THEOREMS.
Sivu 90
... theorem . R 5.6 A related training theorem for the case R = 2 In Chapter 4 we discussed another error - correction procedure , the frac- tional correction rule . In this rule the correction increment at the kth step ck is set equal to ...
... theorem . R 5.6 A related training theorem for the case R = 2 In Chapter 4 we discussed another error - correction procedure , the frac- tional correction rule . In this rule the correction increment at the kth step ck is set equal to ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |