ESVM Algorithm in Transfer Learning Data Classification

doi:10.3969/j.issn.1000-3428.2012.08.057

Computer Engineering ›› 2012, Vol. 38 ›› Issue (08): 173-176. doi: 10.3969/j.issn.1000-3428.2012.08.057

• Networks and Communications • Previous Articles Next Articles

ESVM Algorithm in Transfer Learning Data Classification

ZHANG Jian-jun ¹, WANG Shi-tong ^1,2,3, WANG Jun ^1,2,3

(1. School of Information Technology, Jiangnan University, Wuxi 214122, China; 2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China; 3. National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China)

Received:2011-08-15 Online:2012-04-20 Published:2012-04-20

迁移学习数据分类中的ESVM算法

张建军 ¹，王士同 ^1,2,3，王骏 ^1,2,3

(1. 江南大学信息工程学院，江苏无锡 214122；2. 南京理工大学计算机科学与技术学院，南京 210094； 3. 南京大学计算机软件新技术国家重点实验室，南京 210093)

作者简介:张建军(1987－)，男，硕士研究生，主研方向：模糊聚类；王士同，教授、博士生导师；王骏，讲师、博士研究生

Abstract

Abstract: In transfer learning process, noise makes the result unreasonable when you classify slow changing dataset. Here is an algorithm called Extended Support Vector Machine(ESVM) proposed to solve this problem. Because it makes full use of probability distribution of original data and uses the learning experience of the previous dataset to classify the latter dataset, ESVM can correctly classify the changing dataset with inheriting the characteristics from the previous dataset. Experimental result shows the antinoise performance of the algorithm.

Key words: transfer learning, classification, Support Vector Machine(SVM), inheriting experience, antinoise performance

摘要： 在迁移学习中对变化后的数据集进行分类时，噪音导致分类结果不合理。为此，提出一种迁移学习数据分类中的扩展支持向量机(ESVM)算法。使用变化前数据集的概率分布信息及学习经验，指导缓慢变化后的数据集进行分类，使分割面既可以准确分割现有数据集，同时也保留原先数据集的一些属性。实验结果表明，该算法具有一定的抗噪性能。

关键词: 迁移学习, 分类, 支持向量机, 继承经验, 抗噪性能

CLC Number:

TP18

ZHANG Jian-Jun, WANG Shi-Tong, WANG Jun. ESVM Algorithm in Transfer Learning Data Classification[J]. Computer Engineering, 2012, 38(08): 173-176.

张建军, 王士同, 王骏. 迁移学习数据分类中的ESVM算法[J]. 计算机工程, 2012, 38(08): 173-176.

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http://www.ecice06.com/EN/Y2012/V38/I08/173

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