In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neig...
We improve Gaussian processes (GP) classification by reorganizing the (non-stationary and anisotropic) data to better fit to the isotropic GP kernel. First, the data is partitione...
This paper presents how to extract non-linear features by linear PCA. KPCA is effective but the computational cost is the drawback. To realize both non-linearity and low computati...
Structure and motion estimation from long image sequences is a an important and difficult problem in computer vision. We propose a novel approach based on nonlinear and adaptive ...
Virtually all existing classification techniques label one sample at a time. In this paper, we highlight the potential benefits of group based classification (GBC), where the clas...
In this paper, we present a novel technique to automatically generate optimum classification cascades. Given a powerful classifier SF with satisfactory accuracy and a set of N cla...
Ezzat Ali El-Sherif, Sherif Abdelazeem, M. Fathy A...
In our previous study, we visualized microarray data of hepatocellular carcinoma (HCC) by using selforganizing-map, and investigated molecular signature representing the developme...