>.Please go through a paper "Ensemble of Exemplar-SVMs for Object Detection and Beyond" by Tomasz Malisiewicz (ICCV 2011) Thanks, will go through the paper.
>During testing, a special calibration techniques used to choose the best output. On terms of calibration, differently sized templates will produce different scores so you have to do something like Platt's method. > Why do you want to do that? > To be more specific, how do you quantify success of your task? > By definition, probabilities are "normalized" in the sense that the > are guaranteed to live in the [0-1] range. However the classifier > models can be predict arbitrarily bad probabilities. For instance a > badly trained or badly parameterized binary classifier could predict 0 > proba for the positive class 100% of the time. The following sums the problem: > [Classifiers are trained on similar type datasets, difference being their sizes and the way each result might be used after classification]. I am using SGDClassifier to train the individual classifiers, and need to choose the best amongst them. But as I understand I would need to normalize first before comparing them and was not sure how to calibrate them as such. Any pointers to would be helpful. - I would quantify a most similar document as a successful choice from the results of ensemble of classifiers. I was a bit confused on how to weight the predictions from each classifier so as to compare their values. - The motivation for having multiple classifiers was mostly due to the logical separation of the dataset documents' property in my problem. [From the implementation perspective the training multiple classifiers saves on time (~7 min vs ~17 min) and space (2.2G vs 12G)] --[Apologies on the duplicate thread, seems that it might take more than 6hrs for the post to show up] Thanks, Abhi ------------------------------------------------------------------------------ Rapidly troubleshoot problems before they affect your business. Most IT organizations don't have a clear picture of how application performance affects their revenue. With AppDynamics, you get 100% visibility into your Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics Pro! http://pubads.g.doubleclick.net/gampad/clk?id=84349351&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
