Support vector machines based applications

Tang, L.-J., Zhou, Y.-P., Jiang, J.-H., Zou, H.-Y, Wu, H.-L., Shen, G.-L. and Yu, R.-Q. (2007) Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies./. Chem. Inf. Model, 47,1438-1445. 

The final detected polyps are obtained by application of a statistical classifier based on the image features to the differentiation of polyps from false positives. Investigators use parametric classifiers such as quadratic discriminant analysis (Yoshida and Nappi 2001), non-parametric classifiers such as artificial neural networks (Jerebko et al. 2003b Kiss et al. 2002 Nappi et al. 2004b), a committee of neural networks (Jerebko et al. 2003a), and a support vector machine (Gokturk et al. 2001). In principle, any combination of features and a classifier that provides a high classification performance should be sufficient for the differentiation task. 

Baumes, L.A., Serra, J.M., Serna, P. and Corma, A. (2006). Support vector machines for predictive modelling in heterogeneous catalysis A comprehensive introduction and overfitting estimation based on two real applications, J. Comb. Chem., 8, 583-596. 

C. Guo, Eds., Vol. 3173, Springer, New York, 2004, pp. 512-517. RBF Kernel Based Support Vector Machine with Universal Approximation and its Application. 

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