Research on Methods of Improving the Efficiency of Support Vector Machine in Processing Complex Data View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2013-2016

FUNDING AMOUNT

790000 CNY

ABSTRACT

Support vector machine(SVM), as a general and effective machine learning approach, has been applied successfully in many fields. With the fleetly growing data size and more complex data expression and structure in actual application problems, the learning efficiency and generalization performance of SVM are restricted greatly, and then the application areas are also limited. In this project, the theory and approaches on improving the learning efficiency and generalization performance of SVM for complex data will be studied deeply. The main research contents include: (1) Kernel function selection approach based on semi-parameter. (2) Depth of fusion on SVM and granular computing, which includes reconstructing the optimal quadratic programming problem, analyzing the decision hyperplane in geometry after SVM training and correcting the hyperplane. (3) Effective dimension reduction approach under SVM-like frame. Linear PCA will be learned with linear programming, and the method will be extended to nonlinear/kernel PCA. Also, the algorithms for learning linear and nonlinear PCA will be provided. (4) Researches on constructing semantic feature function for non-structure data and exploring the learning mechanism of structural SVM. (5) Establishment of SVM evaluation system based on parameters of SVM model and learning More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=61273291

Related SciGraph Publications

  • 2016-01. Principal pixel analysis and SVM for automatic image segmentation in NEURAL COMPUTING AND APPLICATIONS
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