—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Machine learning with few training examples always leads to over-fitting problems, whereas human individuals are often able to recognize difficult object categories from only one ...
Statistical learning methods are emerging as a valuable tool for decoding information from neural imaging data. The noisy signal and the limited number of training patterns that ar...
Abstract. Computational analysis of mass spectrometric (MS) proteomic data from sera is of potential relevance for diagnosis, prognosis, choice of therapy, and study of disease act...
Elena Marchiori, Connie R. Jimenez, Mikkel West-Ni...
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...