Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods, from the field of machine learning. In this work we propose a hierarchical classifier (HC) architecture. Moreover, we show that radial basis function-support vector machines may obtain a lower error rate than linear support vector machines, if a step of feature selection and a step of feature transformation is performed. The error rate decreases from 9.1% using linear support vector machines to 6.85% using the new hierarchical classifier. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Nanni, L., & Lumini, A. (2005). Support vector machines for HIV-1 protease cleavage site prediction. In Lecture Notes in Computer Science (Vol. 3523, pp. 413–420). Springer Verlag. https://doi.org/10.1007/11492542_51
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