Pattern Selection for Support Vector Regression based on Sparseness and Variability

Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubic, its use is limited in data mining involving problems with a huge pattern set with a cubic time complexity of its training time. In this paper, we propose a pattern selection method for support vector regression (SVR), using notions of sparseness, variability and uniqueness. Two versions of algorithms, deterministic and stochastic, are presented, which are then applied to an artificial data set and two well known real world data sets. Preliminary results justify further investigation. The proposed method should work well with non-SVM function approximators such as neural networks.