Kernel-based data fusion for machine learning. Methods and applications in bioinformatics and text mining.
The book provides an introduction to data fusion problems using support vector machines (SVMs). In the first part, some learning algorithms in statistics and machine learning are theoretically investigated, the authors showing that many of these algorithms can be formulated as a unified mathematical model -- the Rayleigh quotient -- and can be expressed as dual representations on the basis of kernel methods. The second part of the book is devoted to the development of several novel algorithms for supervised/unsupervised learning. In the third part, the proposed algorithms are substantiated in some real bioinformatics and journal scientometrics applications. The book is meant for researchers, scientists and engineers using SVMs, or other statistical learning methods, but it also may be used as a reference material for graduate courses in machine learning and data mining.
scientific article; zbMATH DE number 5883025