Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 10
Sivu 32
... partitioned by a ( d1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . = 9 Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d 2 as an example . Figure ...
... partitioned by a ( d1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . = 9 Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d 2 as an example . Figure ...
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 XN , 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 XN , still without alter- ing the partition of X ...
Sivu 107
... partition of the subsets X1 and X2 . We now define a nonredundant partition of X1 and X2 as a O ( a ) ( b ) ( c ) ( d ) FIGURE 6.8 Nonredundant ( a , b , c , ) and redundant ( d , e ) partitions ( e ) partition with the property that if ...
... partition of the subsets X1 and X2 . We now define a nonredundant partition of X1 and X2 as a O ( a ) ( b ) ( c ) ( d ) FIGURE 6.8 Nonredundant ( a , b , c , ) and redundant ( d , e ) partitions ( e ) partition with the property that if ...
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 |