Joos P. L. Vandewalle – författare

How does homebanking work? How are board games developed? How reliable can wind energy get? How do we discover forged paintings? Do smart girls stay single? How dangerous can a bioterrorist get? In all these questions (and many others), mathematics plays a crucial role in the search for an answer.​This book tells the story behind twenty of these questions. This is explicitly not a mathematics book, but a book about the crucial role that mathematics plays in devising the creative solutions the world needs. The questions are divided into three categories: mathematics in everyday life, mathematics for the workplace, and mathematics for tomorrow's society. The themes illustrate not only the incredibly broad applicability of mathematics in the world around us, but also the great diversity of useful mathematical techniques.

How does homebanking work? How are board games developed? How reliable can wind energy get? How do we discover forged paintings? Do smart girls stay single? How dangerous can a bioterrorist get? In all these questions (and many others), mathematics plays a crucial role in the search for an answer.​This book tells the story behind twenty of these questions. This is explicitly not a mathematics book, but a book about the crucial role that mathematics plays in devising the creative solutions the world needs. The questions are divided into three categories: mathematics in everyday life, mathematics for the workplace, and mathematics for tomorrow''s society. The themes illustrate not only the incredibly broad applicability of mathematics in the world around us, but also the great diversity of useful mathematical techniques.

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.