Machine learning in neuroimaging based modalities using support vector machines with wavelet kernels
Machine learning in neuroimaging modalities is important for building a successful prediction model, especially in the field of Psychiatry. For example, successful classification of groups, tasks and behaviors leads to the possibility of automated diagnostic detection. Similarly, prediction of a behavioral outcome using regression approaches could provide an insight into a certain behavioral pattern in a patient population. Support vector machine (SVM) has been successfully implemented in neuroimaging classification and regression frameworks. Two popular kernels used in SVM are linear and radial ...