Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
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... response represents a category into which a pattern may be placed , and we shall accordingly label the response values by the integers 1 , . . . , R. It is the purpose of this chapter to develop further this basic model and to introduce ...
... response represents a category into which a pattern may be placed , and we shall accordingly label the response values by the integers 1 , . . . , R. It is the purpose of this chapter to develop further this basic model and to introduce ...
Sivu 100
... response will be +1 . Since P is odd , N can never equal zero or be even . We have assumed that at the kth stage of ... response . The * For simplicity of explanation in describing the operation of a committee machine we assume that a ...
... response will be +1 . Since P is odd , N can never equal zero or be even . We have assumed that at the kth stage of ... response . The * For simplicity of explanation in describing the operation of a committee machine we assume that a ...
Sivu 110
... response of the layered machine . This response is a two - valued function of the binary numbers f1 ( Y ) y1 F u1 f1 ( Y ) Y : F น ; H H ( U ) Response fp ( Y ) Ир Switching function Weights Summing devices Augmented pattern Threshold ...
... response of the layered machine . This response is a two - valued function of the binary numbers f1 ( Y ) y1 F u1 f1 ( Y ) Y : F น ; H H ( U ) Response fp ( Y ) Ир Switching function Weights Summing devices Augmented pattern Threshold ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |