— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Abstract— We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candi...
—Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attribute...
Abstract— In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artif...
Yizhao Ni, Carlton Chu, Craig J. Saunders, John As...
— This paper investigates spatiotemporal feature extraction from temporal image sequences based on invariance representation. Invariance representation is one of important functi...