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- W1807018853 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." @default.
- W1807018853 created "2016-06-24" @default.
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- W1807018853 date "2009-01-01" @default.
- W1807018853 modified "2023-09-23" @default.
- W1807018853 title "Incremental Kernel Machines for Protein Remote Homology Detection" @default.
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- W1807018853 doi "https://doi.org/10.1007/978-3-642-02319-4_49" @default.
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