Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 79
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discriminant functions . Of course , the location and form of the decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without ...
discriminant functions . Of course , the location and form of the decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without ...
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A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
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and for any pattern X producing a U for which H ( U ) H = -1 { g1 ( 1 ) ( X ) } < ( i ( 92 ( X ) ) max i = 1 , ... , L ( 6-15 ) max i = 1 , ... , 2P - L Inequalities ( 6.14 ) and ( 6 · 15 ) lead us to define the discriminant functions ...
and for any pattern X producing a U for which H ( U ) H = -1 { g1 ( 1 ) ( X ) } < ( i ( 92 ( X ) ) max i = 1 , ... , L ( 6-15 ) max i = 1 , ... , 2P - L Inequalities ( 6.14 ) and ( 6 · 15 ) lead us to define the discriminant functions ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |