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Support Vector Machines Based on Reducing Noise

Author: JiangBoDong
Tutor: LuShuXia
School: Hebei University
Course: Applied Mathematics
Keywords: Statistical Learning Theory Support Vector Machine Maximum interval Noise Outliers
CLC: TP181
Type: Master's thesis
Year: 2010
Downloads: 33
Quote: 0
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Abstract


The support vector machine based on statistical learning theory , boils down to a new type of machine learning methods optimization method , support vector machine is based on two types of classification problems , multi- class classification problem is there are still many problems , there are a lot of work to do , and support vector machine is sensitive to noise and outliers , improve noise immunity support vector machine needs further study . For support vector machine is sensitive to noise and outliers , in order to reduce the impact of noise and outliers SVM , proposed a way to reduce the noise impact on support vector machine . Consider the importance of each sample point , a new objective function wrongly classified the degree of sample points were re - description , and control the scope of the error function , such simplified obtained by solving the problem of polygamous separated hyperplane , increasing the hyperplane classification interval , and reduces the number of support vectors , and reducing the running time . The experimental results show that the proposed method is better than the standard support vector machine classification efficiency and the generalization ability .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Automated reasoning,machine learning
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