Name
Title | ||
|---|---|---|
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging |
Abstract | ||
|---|---|---|
We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss function under the assumption that the so- lution of optimal aggregation problem is sparse. We use a boosting type algorithm of optimal aggregation to develop aggregate classifiers of ac- tivation patterns in fMRI based on locally trained SVM classifiers. The aggregation coefficients are then used to design a "boosting map" of the brain needed to identify the regions with most significant im pact on clas- sification. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | convex combination,upper bound,loss function |
Field | DocType | Citations |
Aggregation problem,Mathematical optimization,Pattern recognition,Functional magnetic resonance imaging,Convex combination,Computer science,Support vector machine,Regular polygon,Boosting (machine learning),Artificial intelligence,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.66 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Koltchinskii | 1 | 89 | 9.61 |
| Manel Martínez-ramón | 2 | 216 | 19.66 |
| Posse, Stefan | 3 | 3 | 0.66 |