Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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0 0 equal to zero only for X = When these conditions are met , both 0 the matrix A and the quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the ...
0 0 equal to zero only for X = When these conditions are met , both 0 the matrix A and the quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the ...
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If the responses of at least 12 ( N + 1 ) of these negatively responding TLUs were changed from - 1 to +1 , then the majority of the committee TLUs would have positive responses , and the machine would respond correctly to Yk . For ...
If the responses of at least 12 ( N + 1 ) of these negatively responding TLUs were changed from - 1 to +1 , then the majority of the committee TLUs would have positive responses , and the machine would respond correctly to Yk . For ...
Sivu 101
Thus , if Yk causes a majority of the committee TLUs to respond negatively , we adjust the 2 ( N 1 + 1 ) weight vectors making the leastnegative ( but not positive ) dot products with Yk . If the weight vector W ( k ) is among this set ...
Thus , if Yk causes a majority of the committee TLUs to respond negatively , we adjust the 2 ( N 1 + 1 ) weight vectors making the leastnegative ( but not positive ) dot products with Yk . If the weight vector W ( k ) is among this set ...
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adjusted apply assume bank belonging 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 given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix mean vector 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 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 |