Applications of Regularization
We discuss applications of regularization to bioinformatics and text classification. In both of these very high-dimension domains, we find that using regularization to restore well-posedness to the associated learning problems allows us to overcome the curse of dimensionality and achieve state-of-the-art results. In the bioinformatics domain, we focus on classifying DNA microarray data and finding the relevant genes for the classification function. We also address the issue of statistical significance via permutation tests. In the text classification domain, we show how the use of either the Support Vector Machine or the (much faster) Regularized Least-Squares Classification algorithm produces state-of-the-art results. We find that best results are achieved by using a simple one-vs.-all classification system, and present theoretical arguments to support this finding.