Multivariate Pattern Classification

More documents

Recommendations

Info

SPM 2010 - 13. Kurs zur funktionellen BildgebungClassificationSPM-Kurs Hamburg 2010Simplest Approach: Fisher Linear Discriminant (FLD)FLD classifies by projecting the training set on the axis that is definedby the difference between the center of mass for both classes,corrected by the within class scatterseparation is maximised for:m1−m2w =cov + covclass1class2ClassificationSPM-Kurs Hamburg 2010Linear classificationvoxel 2hyperplanevolume in t 1volume in t 2volume in t 4volume in t 3weightvolume in t 25vector wvoxel 1y = wx + bhyperplane defined by weight vector w andoffset bClassificationSPM-Kurs Hamburg 2010Linear classificationvoxel 2hyperplaneHow to interpret theweight vector?volume in t 1volume in t2volume in t 4volume in t 3volume in t 25weightvector wvoxel 1Weight vector (Discriminating Volume)W = [0.45 0.89] 0.45 0.89The value of each voxel in the weight vector indicates its importance indiscriminating between the two classes (i.e. cognitive states).Pattern Classification10
SPM 2010 - 13. Kurs zur funktionellen BildgebungClassificationSPM-Kurs Hamburg 2010Support Vector Machine (SVM)voxel 2voxel 1Which of the linear separators is the optimal one?ClassificationSPM-Kurs Hamburg 2010Support Vector Machine (SVM)voxel 2marginSVM = maximummargin classifiersupport vectorsvoxel 1If classes have overlapping distributions), SVM’s are modified to accountfor misclassification errors by introducing additional slack variablesClassificationSPM-Kurs Hamburg 2010Linear classifiers Fisher Linear Discriminant Support Vector Machine (SVM) Logistic Regression Gaussian Naive Bayes …Nonlinear classifiers SVM with kernel Neural Networks …How to choose the right classifier?Pattern Classification11
Page 1 and 2: SPM 2010 - 13. Kurs zur funktionell
Page 9: SPM 2010 - 13. Kurs zur funktionell

Multivariate Pattern Classification

Create successful ePaper yourself

Delete template?

Save as template?