The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often ...
Bernard Ng, Arash Vahdat, Ghassan Hamarneh, Rafeef...
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input...
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbone...
Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
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...
In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We present a detailed study of Bregman Distance minimization, a f...