Name
Title | ||
|---|---|---|
Selecting the hypothesis space for improving the generalization ability of Support Vector Machines. |
Abstract | ||
|---|---|---|
The Structural Risk Minimization framework has been recently proposed as a practical method for model selection in Support Vector Machines (SVMs). The main idea is to effectively measure the complexity of the hypothesis space, as defined by the set of possible classifiers, and to use this quantity as a penalty term for guiding the model selection process. Unfortunately, the conventional SVM formulation defines a hypothesis space centered at the origin, which can cause undesired effects on the selection of the optimal classifier. We propose here a more flexible SVM formulation, which addresses this drawback, and describe a practical method for selecting more effective hypothesis spaces, leading to the improvement of the generalization ability of the final classifier. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IJCNN.2011.6033356 | IJCNN |
Keywords | Field | DocType |
pattern classification,support vector machines,SVM formulation,complexity measurement,generalization ability,hypothesis space selection,model selection process,optimal classifier selection,structural risk minimization framework,support vector machines | Structured support vector machine,Least squares support vector machine,Pattern recognition,Computer science,Support vector machine,Model selection,Artificial intelligence,Relevance vector machine,Structural risk minimization,Margin classifier,Classifier (linguistics),Machine learning | Conference |
Citations | PageRank | References |
14 | 0.74 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Luca Oneto | 3 | 830 | 63.22 |
| Sandro Ridella | 4 | 677 | 140.62 |