Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 41
Sivu 4
... important role in the classification to be performed . At best the process can make use of known information about some measurements that are certain to be important . A weather forecaster in the northern hemisphere might know , for ...
... important role in the classification to be performed . At best the process can make use of known information about some measurements that are certain to be important . A weather forecaster in the northern hemisphere might know , for ...
Sivu 28
... important application of quadric surfaces . 2.10 Implementation of quadric discriminant functions There are two important methods of implementing quadric discriminant functions . One is suggested by Eq . ( 2 · 21 ) and will be of importance ...
... important application of quadric surfaces . 2.10 Implementation of quadric discriminant functions There are two important methods of implementing quadric discriminant functions . One is suggested by Eq . ( 2 · 21 ) and will be of importance ...
Sivu 29
... importance and is discussed in detail in the Appendix . To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , , fM are functions d . The first d components of F are x1 , x22 ...
... importance and is discussed in detail in the Appendix . To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , , fM are functions d . The first d components of F are x1 , x22 ...
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 |