Abstract
It is known that Support Vector Domain Description (SVDD) has been introduced to detect novel data or outliers. The key problem of training a SVDD is how to solve constrained quadratic programming (QP) problem. The Linear Particle Swarm Optimization (LPSO) is developed to optimize linear constrained functions, which is intuitive and simple to implement. However, premature convergence is possible with the LPSO. The LPSO is extended to the Converging Liner PSO (CLPSO), which is guaranteed to always find at least a local optimum. A new method using CLPSO to train SVDD is proposed. Experimental results demonstrate that the proposed method is feasible and effective for SVDD training, and its performance is better than traditional method.
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References
Vapnik, V.N.: The nature of statistical learning theory. Springer, Heidelberg (2000)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54, 45–66 (2004)
Vapnik, V.N., Kotz, S.: Estimation of dependences based on empirical data. Springer, New York (2006)
Osuna, E., Freund, R., Girosi, F.: Support vector machines: Training and applications (1997)
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. Citeseer (1997)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization (1999)
Kennedy, J., Eberhart, R.: Particle swarm optimization (1995)
Yuan, H., et al.: A modified particle swarm optimization algorithm for support vector machine training (2006)
Paquet, U., Engelbrecht, A.P.: New particle swarm optimiser for linearly constrained optimization
Paquet, U., Engelbrecht, A.P.: Raining support vector machines with particle swarms (2003)
Paquet, U., Engelbrecht, A.P.: Particle swarms for equality-constrained optimization. Submitted to IEEE Transactions on Evolutionary Computation
Bergh, F., Engelbrecht, A.P.: A new locally convergent particle swarm optimizer (2002)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. IEEE, Piscataway (2000)
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Wang, H., Zhao, G., Li, N. (2010). Training Support Vector Data Descriptors Using Converging Linear Particle Swarm Optimization. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15621-2_23
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DOI: https://doi.org/10.1007/978-3-642-15621-2_23
Publisher Name: Springer, Berlin, Heidelberg
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