Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function

LIN Wei; LIU Ting; LV Wei-guo

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Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function

更新时间：2026-09-04

- Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function
- Vol. 33, Issue 8, Pages: 149-152(2016)
- 作者机构：
  
  哈尔滨工程大学理学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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林蔚, 刘婷, 吕伟国. 大数据中边界向量调节熵函数支持向量机研究[J]. 微电子学与计算机, 2016, 33(8): 149-152, 157.

LIN Wei, LIU Ting, LV Wei-guo. Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function[J]. 2016, 33(8): 149-152.
林蔚, 刘婷, 吕伟国. 大数据中边界向量调节熵函数支持向量机研究[J]. 微电子学与计算机, 2016, 33(8): 149-152, 157. DOI：

LIN Wei, LIU Ting, LV Wei-guo. Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function[J]. 2016, 33(8): 149-152. DOI：

摘要

当训练集的规模很大时

一般的支持向量机的学习过程需要占用大量的内存

寻优速度缓慢

不利于实际应用.提出了一种预抽取支持向量的支持向量机调节熵函数法.首先

利用两凸包相对边界向量方法预抽取出边界向量; 然后

利用支持向量机调节熵函数法来训练预抽取的边界向量.实验表明

采用这种方法来训练样本集不仅降低了学习的代价

还提高了分类速度.

Abstract

When the size of the training set is large

learning process in general support vector machines take a lot of memory

optimizing slow

is not conducive to practical application. This paper presents a boundary vector-based SVM adjustable entropy function method. First

we use two methods convex hull boundary vectors relative pre-extracted boundary vectors; Then

to train pre-drawn boundary vectors in SVM adjustable entropy method. Experiments show that this method not only reduces the training sample set the price of learning

but also improve the classification rate.

关键词

Keywords

references

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