A PSVM and Negative Binomial Model for the Patenting Evolution

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In recent years, evaluating the patenting activity has gained a renewed interest in both growth economists and trade economists. An evolution model of the patenting activity is proposed by applying the potential support vector machine (PSVM) and the negative binomial model in this paper. The PSVM is used to features selection and negative binomial is used to evolution. The proposed method is feasible and effective by the results, and it provides a better forecast and estimate tool for the patenting activity. It also provides a novel way for the evolution design of the other engineering.

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3235-3239

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December 2010

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] V.N. Vapnik: An Overview of Statistical Learning Theory. IEEE Trans. Neural Networks Vol. 5(1999), no. 10, pp.988-999.

DOI: 10.1109/72.788640

Google Scholar

[2] V.N. Vapnik: The Nature of Statistical Learning Theory(Springer, New York (1995).

Google Scholar

[3] C. Huang,C. Wang: A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications. vol. 31 (2006), p.231–240.

DOI: 10.1016/j.eswa.2005.09.024

Google Scholar

[4] Z. Li,X. Tian: Study of Soft Sensor Modeling Method Based on KPCA-SVM. Intelligent Control and Automation (2006), p.4876 – 4880.

DOI: 10.1109/wcica.2006.1713311

Google Scholar

[5] S. Hochreiter, K. Obermayer: Support vector machines for dyadic data. Neural Computation Vol. 18(2006), no. 6, pp.1472-1510.

DOI: 10.1162/neco.2006.18.6.1472

Google Scholar

[6] Xuesong Guo, Zhiping Yuan and Bojing Tian: Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications Vol. 36 (April 2009), Issue 3, Part 2, pp.6978-6985.

DOI: 10.1016/j.eswa.2008.08.074

Google Scholar

[7] I. Guyon, S. Gunn, M. Nikravesh and L. Zadeh:Nonlinear feature selection with the Foundations and Applications in Feature extraction. Eds. Springer (2005).

Google Scholar