Incremental kernel machines for protein remote homology detection

Abstract

Protein membership prediction is a fundamental task to retrieve information for unknown or unidentified sequences. When support vector machines (SVMs) are associated with the right kernels, this machine learning technique can build state-of-the-art classifiers. However, traditional implementations work in a batch fashion, limiting the application to very large and high dimensional data sets, typical in biology. Incremental SVMs introduce an alternative to batch algorithms, and a good candidate to solve these problems. In this work several experiments are conducted to evaluate the performance of the incremental SVM on remote homology detection using a benchmark data set. The main advantages are shown, opening the possibility to further improve the algorithm in order to achieve even better classifiers. © 2009 Springer Berlin Heidelberg.