#1




Q1315 rbf vsm eout=0?
Q1315 EOUT from rbf support vector machine is always 0.and this machine can never be beaten by k mean rbf .... what is the mistake i probably made?

#2




Re: Q1315 rbf vsm eout=0?
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BTW, if your reply would reveal answers (or excluded answers) of a question, please start an *ANSWER* thread for that (see above announcement).
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#3




Re: Q1315 rbf vsm eout=0?
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Sorry for the troubles i made... and thanks for your quick reply The Eout I achieved from RBF kernel vector support machine is always 0. Is correct ? you gave excellent lectures! What book would you recommend for further studying on machine learning , or machine learning in financial field? 
#4




Re: Q1315 rbf vsm eout=0?
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#5




Re: Q1315 rbf vsm eout=0?
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The training data and testing data are totally separate. I used 100 training data to train this rbf vector support machine ,and randomly generate 100 new points for testing. (no matter how many times i randomly generate 100 points , Eout is always 0) 
#6




Re: Q1315 rbf vsm eout=0?
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A zero outofsample error means that the target boundary was perfectly replicated, an illogical event when we train with only 100 points.
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#7




Re: Q1315 rbf vsm eout=0?
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#8




Re: Q1315 rbf vsm eout=0?
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MATLAB CODE %%%% [maxval, maxind] = max(alpha); b=1/y(maxind) kernel_x(maxind,*(alpha.*y); %%%% and i was unable to get w , then Ein was measured by %%% pred=sign(kernel_x*alpha.*y+b); Ein_svm=numel(find(pred~=y)); For test , i used pred=sign(test_kernel_x*alpha.*y+b); Eout_svm=numel(find(pred~=y_test)). as if the number of test data points are not consistent with number of training data points. test_kernel_x*alpha.*y is unable to be calculated , as dimensions do not match....... I think there is some confusion about my understanding to kernel vector machine. isn't w unachievable? Thanks professor. 
#9




Re: Q1315 rbf vsm eout=0?
correction *b=1/y(maxind) kernel_x(maxind,)*(alpha.*y);

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