The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining ...
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Abstract. This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain ...
Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul...
We propose a new low complexity and fast converging frequencydomain adaptive algorithm for sparse system identification. This is achieved by exploiting the MMax and SP tap-select...
Andy W. H. Khong, Xiang Lin, Milos Doroslovacki, P...
We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on spa...