| 一种基于KKT条件和壳向量的SVM增量学习算法 |
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| 引用本文： | 茅嫣蕾,魏赟,贾佳. 一种基于KKT条件和壳向量的SVM增量学习算法[J]. 电子科技, 2016, 29(2): 38 |
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| 作者姓名： | 茅嫣蕾 魏赟 贾佳 |
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| 作者单位： | (1.上海理工大学 光电信息与计算机工程学院,上海 200093;2.上海生物信息技术研究中心,上海 201202) |
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| 基金项目： | 国家自然科学基金资助项目(61170277);上海市教委科研创新基金资助项目(12YZ094) |
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| 摘 要： | 针对传统支持向量机(SVM)增量算法,在学习过程中因基于局部最优解而可能舍弃含隐性信息的非支持向量样本,以及对于新增样本需全部进行训练的缺点,文中提出一种基于KKT条件和壳向量的SVM增量学习算法。该方法利用壳向量的特性保留了训练样本集中可能含隐性信息的非支持向量,并只将违反KKT条件的增量样本加入新的训练集,从而提高运算效率。通过对公共数据集Abalone和 Balance Scale的实验表明,新算法在属性列数较多的数据集上分类效果更明显。
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| 关 键 词： | SVM 增量学习 KKT条件 壳向量 |
A New Incremental SVM Learning Algorithm Based on KKT Conditions and Hull Vectors |
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| MAO Yanlei,WEI Yun,JIA Jia. A New Incremental SVM Learning Algorithm Based on KKT Conditions and Hull Vectors[J]. Electronic Science and Technology, 2016, 29(2): 38 |
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| Authors: | MAO Yanlei WEI Yun JIA Jia |
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| Affiliation: | (1.School of Optical-electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;2.Shanghai Center for Bioinformation Technology,Shanghai 201202,China) |
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| Abstract: | The traditional support vector machine (SVM) incremental algorithm in the learning process may give up the non-support vectors with implicit information,and requires the training of all the incremental samples.This paper presents a new incremental SVM learning algorithm based on KKT conditions and hull vectors.The algorithm makes use of the characteristics of hull vectors to retrain non-support vectors with implicit information,and it only add the samples violating the KKT conditions to the new training set.The experimental results from Abalone dataset and Balance Scale dataset show this algorithm has better classification effect in the datasets with more columns of properties. |
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| Keywords: | SVM incremental learning KKT conditions hull vectors, |
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