2015 年 19 巻 3 号 p. 407-416
This paper proposes wavelet Lp-norm support vector regression (Lp-WSVR) to solve feature selection and regression problems effectively. Unlike conventional support vector regression (SVR), linear Lp-WSVR ensures that useful features are selected based on theoretical analysis. By using the wavelet kernel, Lp-WSVR approaches any curve in quadratic continuous integral space that leads to improving regression performance. Results of experiments show the superiority of Lp-WSVR in both feature selection and regression performances. Applying Lp-WSVR to Chinese real estate prices shows that the most significant and powerful factor contributing to Chinese housing prices is monetary growth.
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