On-Line Support Vector Machine Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Accurate on-line support vector regression
Neural Computation
Profile-Based String Kernels for Remote Homology Detection and Motif Extraction
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
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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.