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

Abstract:

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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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Periodical:

Edited by:

Helen Zhang and David Jin

Pages:

281-288

DOI:

10.4028/www.scientific.net/AMR.382.281

Citation:

B. Yang et al., "Support Vector Machine and its Predicting Stability of Partially Stabilized Zirconia by Microwave Heating Preparation", Advanced Materials Research, Vol. 382, pp. 281-288, 2012

Online since:

November 2011

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Price:

$35.00

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