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Learning smooth models of nonsmooth functions via convex optimization
2012
2012 IEEE International Workshop on Machine Learning for Signal Processing
This paper proposes a learning framework and a set of algorithms for nonsmooth regression, i.e., for learning piecewise smooth target functions with discontinuities in the function itself or the derivatives at unknown locations. In the proposed approach, the model belongs to a class of smooth functions. Though constrained to be globally smooth, the trained model can have very large derivatives at particular locations to approximate the nonsmoothness of the target function. This is obtained
doi:10.1109/mlsp.2012.6349755
dblp:conf/mlsp/LauerLB12
fatcat:ibet2qkkqbae7mhexvaccifip4