Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
Data mining applications analyze large collections of set data and high dimensional categorical data. Search on these data types is not restricted to the classic problems of minin...
In this paper we provide a general classification of mathematical optimization problems, followed by a matrix of applications that shows the areas in which these problems have bee...
In the mathematical modeling of real-life applications, systems of equations with complex coefficients often arise. While many techniques of numerical linear algebra, e.g., Krylovs...