초록 열기/닫기 버튼
The goal of multi-output regression is to predict multiple realvalued output variables. Research in this area is lacking compared to multi-output classification . In this approach multi-output kernel regression based on kernel regression in which different outputs are assumed to be correlated. We also introduce a modelselection method employs generalized crossvalidation function for choosing optimal values of hyperparameters. Numerical results from synthetic and real datasets are then obtained to illustrate that the proposed outperforms the other methods on multi-output regression problems.
