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Reconstruction Model of Support Vector Machine and Research on SMO Algorithm

Author: SunShuWei
Tutor: ZhouShuiSheng
School: Xi'an University of Electronic Science and Technology
Course: Applied Mathematics
Keywords: Support Vector Machine SMO Mercer kernel function VC dimension
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 197
Quote: 1
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Abstract


Support Vector Machine is a machine learning branch developed in recent years , more and more attention as a novel and unique machine learning methods . Support vector machine as a powerful learning tool has become a hot discussion and research , it is very effective in dealing with the field of classification, regression and has great potential for future development . Launched a support vector machine and its mathematical model . First introduces the research background and significance of machine learning , support vector machine developments summarized the main content and algorithms . Describes the specific steps of the algorithm and its working set selection in SMO algorithm . Followed in reproducing kernel Hilbert (RKHS) space , the regeneration of the use of nuclear function reconstruction support vector machine model and introduce errors in the model can be extended to the new quadratic programming model . Understanding of the model to reduce limit the requirements applicable to a wider range of situations . Solve the model , the direct decomposition of the traditional and SMO method . The idea of the direct decomposition method is a direct decomposition of the matrix , and then carry out the analytical solution of inversion operations model . The SMO algorithm used in each run only two high -dimensional matrix is optimized to reduce the storage space . As in the first iteration of the process , only two-dimensional quadratic programming optimization problem , can be directly solved analytically , which reduces the algorithm running time . The SMO method using caching techniques , the record has been calculated that the column , to reduce the amount of nuclear matrix calculation , and to improve the efficiency of the algorithm . The experiments show that the SMO method and compared to the direct decomposition method , can greatly increase the number of dimensions of the matrix , and to accelerate the operation speed . Caching technology can reduce the time to calculate the large matrices , greatly improve the computational efficiency .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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