Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 44
... step 1 . There are some important problems in pattern classification in which these steps can be easily applied . This chapter is devoted to a study of the parametric training method as it is used in these problems . 3.2 Discriminant ...
... step 1 . There are some important problems in pattern classification in which these steps can be easily applied . This chapter is devoted to a study of the parametric training method as it is used in these problems . 3.2 Discriminant ...
Sivu 82
... steps ; Sŵ will have no repetitions and will therefore terminate at the koth step if Ŵko + 1 is a weight vector which satisfies inequality ( 5-6 ) . Theorem 5.1 will be proved if we prove that S✩ terminates . • " 5.3 Proof 1 The ...
... steps ; Sŵ will have no repetitions and will therefore terminate at the koth step if Ŵko + 1 is a weight vector which satisfies inequality ( 5-6 ) . Theorem 5.1 will be proved if we prove that S✩ terminates . • " 5.3 Proof 1 The ...
Sivu 123
... step . Suppose that the ( k + 1 ) st pattern in the training sequence is Xx + 1 , a member of category i . Which of the weight vectors belonging to the ith bank is the closest to X + 1 can now be determined , using the PWL machine ...
... step . Suppose that the ( k + 1 ) st pattern in the training sequence is Xx + 1 , a member of category i . Which of the weight vectors belonging to the ith bank is the closest to X + 1 can now be determined , using the PWL machine ...
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 |