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.

• Type: Conference
• Year: 2009
• Where: HAIS
• Authors: Lionel Morgado, Carlos Pereira
• Keywords: Artificial Intelligence, Data Set, HAIS 2009, Incremental Svm, Protein Membership Prediction