Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 32
Sivu 3
... classifier a device with d input lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * x1 x ; : : 1ο = 1 or 2 or 3 or ... or R ... PATTERN CLASSIFIERS 3.
... classifier a device with d input lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * x1 x ; : : 1ο = 1 or 2 or 3 or ... or R ... PATTERN CLASSIFIERS 3.
Sivu 7
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 ... PATTERN CLASSIFIERS 7.
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 ... PATTERN CLASSIFIERS 7.
Sivu 9
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We shall assume here that little if any a ... classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We shall assume here that little if any a ... classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components 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 step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |