Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 5
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
Sivu 71
... described , the value of c determines . how far the weight point is moved . We have distinguished three cases . In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same ...
... described , the value of c determines . how far the weight point is moved . We have distinguished three cases . In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same ...
Sivu 104
... described as follows . The first layer transforms X1 and X2 into 1 ( 1 ) and 2 ( 1 ) , respectively . The second layer transforms 1 ( 1 ) and 2 ( 1 ) into 91 ( 2 ) and 2 ( 2 ) , respectively , and so on , until finally the ( N − 1 ) th ...
... described as follows . The first layer transforms X1 and X2 into 1 ( 1 ) and 2 ( 1 ) , respectively . The second layer transforms 1 ( 1 ) and 2 ( 1 ) into 91 ( 2 ) and 2 ( 2 ) , respectively , and so on , until finally the ( N − 1 ) th ...
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 |