Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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That is , a majority of the weight vectors have negative dot products with Yk . Let
the weight vectors at this stage be given by W / ( k ) , W2 ( k ) , . . . , and W pk ) . In
describing the rule for modifying the weight vectors we shall make use of the ...
That is , a majority of the weight vectors have negative dot products with Yk . Let
the weight vectors at this stage be given by W / ( k ) , W2 ( k ) , . . . , and W pk ) . In
describing the rule for modifying the weight vectors we shall make use of the ...
Sivu 101
Thus , if Yk causes a majority of the committee TLUs to respond negatively , we
adjust the Y2 | | Nal + 1 ) weight vectors ... Those 12 ( Nxl + 1 ) having the least -
positive ( but not negative ) dot products are adjusted by the rule W , ( k + 1 ) = 0 ,
k ...
Thus , if Yk causes a majority of the committee TLUs to respond negatively , we
adjust the Y2 | | Nal + 1 ) weight vectors ... Those 12 ( Nxl + 1 ) having the least -
positive ( but not negative ) dot products are adjusted by the rule W , ( k + 1 ) = 0 ,
k ...
Sivu 127
APPENDIX AN ALTERNATIVE IMPLEMENTATION OF QUADRIC
DISCRIMINANT FUNCTIONS A : 1 Separation of a quadratic form into positive
and negative parts Consider the quadric function g ( x ) = X ' AX + BX + C . ( A : 1 )
where A is a ...
APPENDIX AN ALTERNATIVE IMPLEMENTATION OF QUADRIC
DISCRIMINANT FUNCTIONS A : 1 Separation of a quadratic form into positive
and negative parts Consider the quadric function g ( x ) = X ' AX + BX + C . ( A : 1 )
where A is a ...
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Sisältö
Preface vii | 7 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X 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 Stanford step 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 |