The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Sparse on-line Gaussian processes
Neural Computation
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Accurate on-line support vector regression
Neural Computation
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Leave-One-Out Bounds for Support Vector Regression Model Selection
Neural Computation
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Training a Support Vector Machine in the Primal
Neural Computation
Step Size Adaptation in Reproducing Kernel Hilbert Space
The Journal of Machine Learning Research
IEEE Transactions on Signal Processing
Support vector method for robust ARMA system identification
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
Online estimation of regression functions becomes important in presence of drifts and rapid changes in the training data. In this article we propose a new online training algorithm for SVR, called Priona, which is based on the idea of computing approximate solutions to the primal optimization problem. For the solution of the primal SVR problem we investigated the trade-off between computation time and prediction accuracy for the gradient, diagonally scaled gradient, and Newton descent direction. The choice of a particular buffering strategy did not influence the performance of the algorithm. By using a line search Priona does not require a priori selection of a learning rate which facilitates its practical application. On various benchmark data sets Priona is shown to perform better in terms of prediction accuracy in comparison to the Norma and Silk online SVR algorithms. Further, tests on two artificial data sets show that the online SVR algorithms are able to track temporal changes and drifts of the regression function, if the buffer size and learning rate are selected appropriately.