Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 11
8 Summary of book by chapters In the next chapter we discuss several families of
discriminant functions as possible candidates for use in a pattern - classifying
machine . We examine the properties of some in detail , and present block ...
8 Summary of book by chapters In the next chapter we discuss several families of
discriminant functions as possible candidates for use in a pattern - classifying
machine . We examine the properties of some in detail , and present block ...
Sivu 12
Bahadur , & Lewis , ' and Marill and Green 10 propose and discuss tests for the "
effectiveness ” of measurements . Miller11 illustrates a ... measurement devices
for optical character recognition are discussed in a book edited by Fischer et al .
Bahadur , & Lewis , ' and Marill and Green 10 propose and discuss tests for the "
effectiveness ” of measurements . Miller11 illustrates a ... measurement devices
for optical character recognition are discussed in a book edited by Fischer et al .
Sivu 66
A clearer picture of the precise effects of these weight adjustments is provided by
an alternative geometric representation of the TLU , which will be discussed next
. 4 . 2 Weight space Before discussing training methods for a TLU it will be ...
A clearer picture of the precise effects of these weight adjustments is provided by
an alternative geometric representation of the TLU , which will be discussed next
. 4 . 2 Weight space Before discussing training methods for a TLU it will be ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 solution space specific Stanford step 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 |