Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 20
... exist such that · • 9 XR are gi ( X ) > g ; ( X ) j = 1 ,. , R , ji for all X in Xi for all i = 1 , ( 2.9 ) R " = As ... exists such that 2. We say that a dichotomy if and g ( X ) > 0 g ( X ) < 0 for all X in X1 for all X in X2 ( 2.10 ) ...
... exist such that · • 9 XR are gi ( X ) > g ; ( X ) j = 1 ,. , R , ji for all X in Xi for all i = 1 , ( 2.9 ) R " = As ... exists such that 2. We say that a dichotomy if and g ( X ) > 0 g ( X ) < 0 for all X in X1 for all X in X2 ( 2.10 ) ...
Sivu 99
... exist any training theorems for committee machines . parallel to those given in Chapter 5 for machines . There does exist , however , a training procedure which has proven satisfactory in a variety of different experiments . This ...
... exist any training theorems for committee machines . parallel to those given in Chapter 5 for machines . There does exist , however , a training procedure which has proven satisfactory in a variety of different experiments . This ...
Sivu 120
... exist a simple nonparametric rule , however , whose use implies only that the probability - density functions exist and that they are continuous . We shall call this rule the Fix and Hodges method.2 To determine the discriminant ...
... exist a simple nonparametric rule , however , whose use implies only that the probability - density functions exist and that they are continuous . We shall call this rule the Fix and Hodges method.2 To determine the discriminant ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 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 |