| Title |
Design of SVM-Based Polynomial Neural Networks Classifier Using Particle Swarm Optimization |
| Authors |
노석범(Seok-Beom Roh) ; 오성권(Sung-Kwun Oh) |
| DOI |
http://doi.org/10.5370/KIEE.2018.67.8.1071 |
| Keywords |
Support vector machine ; Polynomial neural networks ; Dynamically generated networks ; Support vector machine based polynomial neural networks ; Multi-objective particle swarm optimization |
| Abstract |
In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers. |