Abstract
Support vector machine (SVM) has been recently proposed as a new technique for classification tasks. However, assigning a specific label to the suitable class is a hard matter, especially when the risk of assigning simultaneously a label to many homogenous classes is high. It was designed for linearly separable classification and has been developed to find the good separation hyper plane between two or many classes thanks to the identification of the most significant training samples of the side of a class. In this paper, we use a nonlinear SVM for 3D object-parts classification, where we use the radial basis function (RBF) as a kernel method to simulate the projection of data into higher dimension space. The advantage of such classifier is the ease of training and testing. The excellent recognition rate achieved in the performed experiments proves that the SVM classifier is one of the most applicable classifiers for 3D object-parts classification.
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Herouane, O., Hardi, H., Moumoun, L. (2018). 3D Object-Parts Classification Based on a Multiclass-SVM Approach. In: Noreddine, G., Kacprzyk, J. (eds) International Conference on Information Technology and Communication Systems. ITCS 2017. Advances in Intelligent Systems and Computing, vol 640. Springer, Cham. https://doi.org/10.1007/978-3-319-64719-7_30
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DOI: https://doi.org/10.1007/978-3-319-64719-7_30
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