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 , Nk can never equal zero or be even . . " - We have assumed that at the kth ... response . The * For simplicity of explanation in describing the operation of a committee machine we assume that a dot ...
... response will be +1 . Since P is odd , Nk can never equal zero or be even . . " - We have assumed that at the kth ... response . The * For simplicity of explanation in describing the operation of a committee machine we assume that a dot ...
Sivu 110
... response of the layered machine . This response is a two - valued function of the binary numbers y1 f1 ( Y ) F u1 y 2 f , ( Y ) Y f น ; H H ( U ) Response ษ fp ( Y ) Weights F Ир Switching function Summing devices Augmented pattern ...
... response of the layered machine . This response is a two - valued function of the binary numbers y1 f1 ( Y ) F u1 y 2 f , ( Y ) Y f น ; H H ( U ) Response ษ fp ( Y ) Weights F Ир Switching function Summing devices Augmented pattern ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |