Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 58
... probability distributions for M ; and Σ ; it is meaning- less to speak of optimum estimates . derived from the training set as if they were the 58 PARAMETRIC TRAINING METHODS Learning the mean vector of normal patterns,
... probability distributions for M ; and Σ ; it is meaning- less to speak of optimum estimates . derived from the training set as if they were the 58 PARAMETRIC TRAINING METHODS Learning the mean vector of normal patterns,
Sivu 59
... mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only ...
... mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only ...
Sivu 61
... mean vector . We note the asymptotic results lim N → ∞ = UN ( X ) lim KN = 0 N → ∞ ( 3.51 ) Further insight into the process of learning the mean vector can be obtained by considering the special case where K = ( 1 / a ) , where a ...
... mean vector . We note the asymptotic results lim N → ∞ = UN ( X ) lim KN = 0 N → ∞ ( 3.51 ) Further insight into the process of learning the mean vector can be obtained by considering the special case where K = ( 1 / a ) , where a ...
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 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 second layer shown in Fig solution weight vectors Stanford 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 |