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Dear Steven, I've got 3070 training examples, including 1801 negative and 1269 positive. Your suggestions are very welcome. Thanks Alireza Steven Barrett wrote: > > Alireza, > > ....just one question - are you using a balanced training set ? > > Steven > > > > "Martin Sewell" <[log in to unmask]> Sent by: "The Support Vector Machine discussion list" To: <[log in to unmask]> SUPPORT-VECTOR-MACHINES 23-Apr-2002 20:46 cc: Please respond to "The Support Vector Subject: Machine discussion list" Re: Help <[log in to unmask]> > > Alireza Osareh asked me to forward the following message to this list. > > At 09:54 23/04/2002 +0100, Alireza Osareh wrote: > >Hello, > > > >I have a classification problem and used Kernel-Adatron Algorithm > >without bias for the training purpose. I obtained reasonable results > >based on Gaussian kernel and also when I considered the same upper > bound > > > >(C) restriction value to both (+1 (positive)) and (-1 (negative)) > >classes. > > > >However, to control the sensitivity and specificity of the SVM, I > apply > > > >an upper bound only on (+1) class (C+) without any upper bound on the > > >(-1) class, so I expect to have more specificity and less sensitivity > > >compare to the initial results (and vice versa when considering > >restriction only on (-1) class). > > > >But, I do not know why the SVM give me always 100% Sensitivity and 0% > > >Specificity, irrespective of the value of C+. To evaluate these > results > >I assumed the output threshold equal to 0. In this case although I > have > >examples from +1 and -1 classes in my test set, the output is always > a > >positive value for each test examples and therefore based on the > output > >threshold (0) I'll get 100% Sensitivity .... > > > >This case is also happened in the opposite way, when I put an upper > >bound on (-1) class and no restriction on (+1) class. > > > >Does anyone know why I've got these wrong results? Any idea will be > >greatly appreciated. > > > >Thanks a lot in advance. > > > >Alireza Assareh > > >