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

Nanni, L., Lumini, A. (2005). Support Vector Machines For Hiv-1 Protease Cleavage Site Prediction. ESTORIL : Springer.

Support Vector Machines For Hiv-1 Protease Cleavage Site Prediction

NANNI, LORIS;LUMINI, ALESSANDRA
2005

Abstract

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
2005
Pattern Recognition and Image Analysis Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceeding, Part II
413
420
Nanni, L., Lumini, A. (2005). Support Vector Machines For Hiv-1 Protease Cleavage Site Prediction. ESTORIL : Springer.
Nanni, Loris; Lumini, Alessandra
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/6796
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