Support vector regression (SVR)
The theoretical background of SVR is similar to that of SVM (Smola et al.; Vapnik 1995; Yuan et al. 2004). In SVR, the kernel function is used to map the vectors into a higher dimensional feature space and linear regression is then conducted in this space. The optimal regression function can be represented by:
where y represents the predicted value of a biological property, and the coefficients alpha, alpha* and bias b are determined by maximizing the following Langrangian expression:
under the following conditions:
References
- Smola AJ and Scholkopf B A tutorial on support vector regression. NeuroCOLT2 Technical Report NC2-TR-1998-030.
- Vapnik VN (1995). The nature of statistical learning theory. New York, Springer.
- Yuan Z and Huang BX (2004). Prediction of protein accessible surface areas by support vector regression. Proteins 57(3): 558-564.