Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
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 84
... steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a solution vector W. It should also be pointed out that ...
... steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a solution vector W. It should also be pointed out that ...
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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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |