Multiple kernel learning is a model to merge multiple kernels by linear combination. Mostly solving the models are slow due to explicit computation of kernels. Here, we propose to approximate kernel map function explicitly in finite dimensional space. Then, we use dual coordinate descent to solve the SVM. By storing the solutions in primal, we do not have to compute the kernel explicitly. A group lasso regularization on kernel weights is solved with SVM alternatingly. This is a side-project in my research projects with Dr. Yi-Ren Yeh and Dr. Frank Wang in Academia Sinica.

lib-mkl
.......\libsvmread.c
.......\libsvmread.mexw64
.......\libsvmwrite.c
.......\libsvmwrite.mexw64
.......\linear.obj
.......\linear_model_matlab.c
.......\linear_model_matlab.h
.......\linear_model_matlab.obj
.......\make.m
.......\Makefile
.......\map
.......\...\libvl.so
.......\...\logistic.m
.......\...\poly2dense.m
.......\...\vl_homkermap.mexa64
.......\predict.c
.......\predict.mexw64
.......\predict_mkl.m
.......\README
.......\result.html
.......\run.m
.......\run_mkl.m
.......\startup.m
.......\train.c
.......\train.mexw64
.......\train_feature_select.m
.......\train_mkl.m
.......\tron.obj

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