Loading...
Please wait, while we are loading the content...
Similar Documents
Multi-objective programming in SVMs (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bi, Jinbo |
| Description | We propose a general framework for support vector machines (SVM) based on the principle of multi-objective optimization. The learning of SVMs is formulated as a multiobjective program (MOP) by setting two competing goals to minimize the empirical risk and minimize the model capacity. Distinct approaches to solving the MOP introduce various SVM formulations. The proposed framework enables a more effective minimization of the VC bound on the generalization risk. We develop a feature selection approach based on the MOP framework and demonstrate its effectiveness on hand-written digit data. 1. |
| File Format | |
| Language | English |
| Publisher | AAAI Press |
| Publisher Date | 2003-01-01 |
| Publisher Institution | Machine Learning, Proceedings of the 20th International Conference (ICML 2003), AAAI |
| Access Restriction | Open |
| Subject Keyword | Mop Framework Multi-objective Programming Hand-written Digit Data General Framework Support Vector Machine Model Capacity Multiobjective Program Generalization Risk Effective Minimization Vc Bound Multi-objective Optimization Feature Selection Approach Empirical Risk Various Svm Formulation |
| Content Type | Text |
| Resource Type | Article |
