This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing thi...
This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass featu...
The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel technique...
Alexander Gammerman, Yuri Kalnishkan, Vladimir Vov...
This paper deals with the problem of making predictions in the online mode of learning where the dependence of the outcome yt on the signal xt can change with time. The Aggregating...
Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are ...