Seminarda professor Adil Bagirov "Nonsmooth DC optimization support vector machines method for piecewise linear regression" mövzusunda məruzə etdi.
We present a new regression method called the adaptive piecewise linear support vector regression (A-PWLSVR). We use the L1-risk function to define regression errors and apply the support vector machine approach in combination with the piecewise linear regression to develop a model for regression problems. We formulate the model as an unconstrained nonconvex nonsmooth optimization problem, where the objective function is represented as a difference of two convex (DC) functions. To address the nonconvexity of the problem a novel incremental approach is proposed. This approach builds the piecewise linear estimates by applying an adaptive selection procedure for the model parameters. The proposed A-PWLSVR method is evaluated on several synthetic and real-world data sets for regression and compared with some mainstream regression methods.
