In many machine learning applications, like Brain - Computer Interfaces (BCI), only high-dimensional noisy data are available rendering the discrimination task non-trivial. In thi...
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
When training Support Vector Machine (SVM), selection of a training data set becomes an important issue, since the problem of overfitting exists with a large number of training da...
Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively...
Classification of microarray data requires the selection of a subset of relevant genes in order to achieve good classification performance. Several genetic algorithms have been d...