This paper describes a novel approach to nd a tighter bound of the transformation of the Min-Max problems into the one of Least-Square Estimation. It is well known that the above ...
—In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the ...
Model selection by the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estima...
System design often explores optimality of performance. What is optimal is, however, often not predefined or static in most cases, because it is affected by the context of operat...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...