This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
The well-known and very simple MinOver algorithm is reformulated for incremental support vector classification with and without kernels. A modified proof for its O(t-1/2 ) converge...
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the stati...
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...