Name
Abstract | ||
|---|---|---|
This research studies a practical iterative algorithm for m ulti-class kernel logistic regression (KLR) (2,3). Opposed to an empirically risk minimization approach such as employed by Support Vector Machines (SVMs), LR and KLR yield probabilistic outcomes based on a maximum likelihood argument. It seen that this framework provides a natural extension to multiclass c lassification tasks, which must be contrasted to the commonly used coding approach. Starting from the negative penalized log likelihood criterium we show that the optimization problem in each iteration can be solved by a weighted version of Least Squares Support Vector Machines (LS-SVMs). In this derivation it turns out that the global regularization term is reflected as a usual regularization in each separate step. In the LS-SVM framework (1), fixed-size LSSVM is known to perform well on large data sets. We therefore implement this model to solve large scale multi- class KLR problems with estimation in the primal space. To reduce the size of the Hessian, an alternating descent version of Newtons method is used which has the extra advantage that it can be easily used in a distributed computing environment (4). It is investigated how a multi-class kernel logistic regression model compares to a one-versus-all coding scheme. The proposed algorithm is compared to existing algorithms using small size to large scale benchmark data sets. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IJCNN.2007.4371223 | Orlando, FL |
Keywords | Field | DocType |
Newton method,regression analysis,support vector machines,LS-SVM framework,Newton's method,distributed computing,iterative algorithm,least squares support vector machine,multiclass kernel logistic regression | Least squares,Regression analysis,Computer science,Hessian matrix,Regularization (mathematics),Artificial intelligence,Optimization problem,Mathematical optimization,Pattern recognition,Least squares support vector machine,Iterative method,Support vector machine,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7576 E-ISBN : 978-1-4244-1380-5 | 978-1-4244-1380-5 | 13 |
PageRank | References | Authors |
0.78 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Karsmakers | 1 | 18 | 1.85 |
| Kristiaan Pelckmans | 2 | 13 | 0.78 |
| Johan A. K. Suykens | 3 | 635 | 53.51 |