- Volume 14 Issue 1
Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.
- Advances in Neural Information Proceeding Systems Transductive Inference for Estimating Values of Functions Chapelle, O.;Vapnik, V.;Weston, J.
- Technometrics v.40 no.4 Prediction Intervals for Neural Networks via Nonlinear Regression DeVeaux, R.;Schumi, J.;Schweinsberg, J.;Shellington, D.;Ungar, L.H.
- ISIS Technical Report, U. of Southampton A Probabilistic Framework for SVM Regression and Error Bar Estimations Gao, J. B.;Gunn, S. R.;Harris, C. J.;Brown, B.
- Proceedings of the 15th International Conference on Machine Learning Ridge Regression Learning Algorithm in Dual Variables Saunders, C.;Gammerman, A.;Vobk, V.
- Communications in Statistics: Theory and Methods v.31 no.10 Prediction Intervals for Support Vector Machine Regression Seok, K.;Hwang, C.;Cho, D.
- The Nature of Statistical Learning Theory Vapnik, V.
- Statistical Learning Theory Vapnik, V.