> Is it the case that we can use any SVM w/ multi-class data, and that > the SVM implementation will handle this case correctly, or do we have > to do pair-wise splits beforehand to reduce it to a two-class problem?
some SVM implementations (native libsvm backend, and libsvm and gmnp in shogun) have built-in pair-wise + voting multiclass SVM implementation. For the others indeed you would need to use MultiClassClassifier. If you just want to try multiclass -- we recommend starting with SMLR -- it has built-in multiclass with 1-vs-rest kind of selection of the decision value > The reason I ask (and am concerned) is that I have written a project > which uses PyMVPA. that is great! ;-) > Now, while the feature selection seems to work, students have reported > that they get the same classification error using a Linear C-SVM, even > when they drastically change the C value (from 10^-7 all the way to > 10^10). nothing can be better for bug-shooting than real user's use cases ;-) it is really weird of what has happened and I guess knowing what exactly SVM implementation was used and either data wasn't bogus (print dataset.summary()) might help to reveal the problem... also I guess I need to check the code later on to assure you that internally everything works ok ;-) also if performance is at chance -- then varying C wouldn't help much ;) > Furthermore, the accuracy was the same for two different > random data subsets. the same at chance? the same perfect? or the same random number? were decision values the same? > This sounds bad to me, but I'm not exactly sure > why, and I thought that the two vs. many classes issue might be a > source of problems. many things could be the source... in such cases make them both run it with MVPA_DEBUG=.* your_analysis 1>analysisX_debug.log 2>&1 and send me both of those _debug.log files -- may be they could highlight the difference? > Any help would be greatly appreciated! > Thanks, > James. > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa -- Yaroslav Halchenko Research Assistant, Psychology Department, Rutgers-Newark Student Ph.D. @ CS Dept. NJIT Office: (973) 353-1412 | FWD: 82823 | Fax: (973) 353-1171 101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102 WWW: http://www.linkedin.com/in/yarik _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
