Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 32
... partitioned by a ( d — 1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d = 2 as an example ...
... partitioned by a ( d — 1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d = 2 as an example ...
Sivu 34
... partitions X ' and suppose that H ; can be made to pass through Xy without altering the partition of X ' . The hyperplane H ; can now be moved to one of two positions with respect to Xx , still without alter- ing the partition of X ...
... partitions X ' and suppose that H ; can be made to pass through Xy without altering the partition of X ' . The hyperplane H ; can now be moved to one of two positions with respect to Xx , still without alter- ing the partition of X ...
Sivu 108
... partition shown in Fig . 6.7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) would suffice to partition the ...
... partition shown in Fig . 6.7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) would suffice to partition the ...
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 |