Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... regions which we shall call decision regions . The ith region R , is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
... regions which we shall call decision regions . The ith region R , is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
Sivu 67
... region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern hyperplanes , we obtain the number of dichotomies of N d ...
... region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern hyperplanes , we obtain the number of dichotomies of N d ...
Sivu 68
... regions on the Nth hyper- plane divides one of the original R ( N - 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N1 , D – 1 ) new regions . This fact gives ...
... regions on the Nth hyper- plane divides one of the original R ( N - 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N1 , D – 1 ) new regions . This fact gives ...
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 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 |