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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 382Support Vector Machine and its Predicting...

Support Vector Machine and its Predicting Stability of Partially Stabilized Zirconia by Microwave Heating Preparation

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Abstract:

Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVR machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave preparing partially stabilized zirconia (PSZ) and built the stability prediction model, the better prediction accuracy and the better fitting results are verified and analyzed. This is conducted to elucidate the good generalization performance of SVMs, specially good for dealing with nonlinear data.

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Advanced Research on Advanced Structure, Materials and Engineering

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Periodical:

Advanced Materials Research (Volume 382)

Pages:

281-288

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.382.281

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Cite this paper

Online since:

November 2011

Authors:

Biao Yang, Wei Li, Li Jun Liu, Jin Hui Peng, Li Bo Zhang, Shi Min Zhang, Sheng Hui Guo

Keywords:

Micro-Wave, Partially Stabilized Zirconia, Prediction, Stability, Support Vector Machine (SVM)

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[1] J. ROBERT KELLY，ISABELLE DENRY: Stabilized zirconia as a structural ceramic: An overview. Dent. Mater. Vol. 24 (2007), pp.289-298.

DOI: 10.1016/j.dental.2007.05.005

[2] V. V. Silva, F. S. Lameiras: Synthesis and characterization of composite powders of partially stabilized zirconia and hydroxyapatite. Materials Characterization. Vol. 45 (2000), pp.51-59.

DOI: 10.1016/s1044-5803(00)00048-6

[3] Anirudh P. Singh, Navdeep Kaur, Ajay Kumar: Preparation of fully cubic calcium-stabilized zirconia with 10 mol% calcium oxide dopant concentration by microwave processing. The American Cermic Society. Vol. 90 (2007), pp.789-96.

DOI: 10.1111/j.1551-2916.2006.01379.x

[4] R.S. Lima, A. Kucuk and C.C. Berndt: Integrity of nanostructured partially stabilized zirconia after plasma spray processing. Materials Science and Engineering. Vol. 313 (2001), pp.75-82.

DOI: 10.1016/s0921-5093(01)01146-7

[5] C.R. Chen, S.X. Li and Q. Zhang: A-Struct. Mater. Prop. Microstruct. Process. Materials Science and Engineering. Vol. 272 (2001), pp.398-409.

[6] L. Haoa, J. Lawrence, G.C. Limb and H.Y. Zheng: Opt. Lasers Eng. Vol. 42 (2004), pp.355-374.

[7] Sheng-hui GUO, Guo CHEN, Jin-hui PENG et al.: preparation of partially stabilized zirconia from zirconia using roasting. Journal of Alloys and Compounds. Vol. 506 (2010), p. L5-L7.

DOI: 10.1016/j.jallcom.2010.06.156

[8] Montross CS: Comparison of bulk properities of Mg-PSZ with temperature-time contour diagrams. Am Ceram Soc. Vol. 76 (1993), p.1993-(1997).

DOI: 10.1111/j.1151-2916.1993.tb08322.x

[9] Hongdong Li, Yizeng Liang, Qingsong Xu: Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems. 95 (2009), pp.188-198.

DOI: 10.1016/j.chemolab.2008.10.007

[10] V. Vapnik: The Nature of Statistical Learning Theory. 2rd ed. (Springer Publications, New York 1999).

[11] V. Vapnik: Statistical Learning Theory. (Wiley Publication, New York 1998).

[12] C. Cortes, V. Vapnik: Mach. Learn. Vol. 20 (1995), pp.273-97.

[13] N. Cristianini and J. Shawe-Taylor: An Introduction to Support Vector Machines. (: Cambridge University Press Publications, Cambridge 2000).

[14] C. Cortes: Prediction of generalization ability in learning machines. USA: university of Rochester, department of computer science, PhD thesis; (1995).

[15] S.T. Wang, K.F.L. Chung and Z.H. Deng et al.: Robust fuzzy clustering neural network based on insensitive loss function. Applied Soft Computing Vol. 7 (2007), pp.577-584.

DOI: 10.1016/j.asoc.2006.04.008

[16] C.C. Chang, C.J. Lin: LIBSVM—A Library for Support Vector Machines. Information on http: /www. csie. ntu. tw/~cjlin/libsvm Software; (2001).

[17] GA Toolbox v1. 2. Information on http: /www. shef. ac. uk/acse/research/ecrg/getgat. html. Software; (2011).