.png){kind=icon}
返回
A BA-based algorithm for parameter optimization of Support Vector Machine
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2016.10.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Optimization algorithms
Support Vector Machine (SVM)
Classification
Parameter optimization
Swarm intelligent
Bat algorithm (BA)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
Particle swarm optimization for parameter determination and feature selection of support vector machines粒子群优化算法在支持向量机参数确定与特征选择中的应用
PREAI
分享收藏An ACO-based algorithm for parameter optimization of support vector machines一种基于蚁群算法的支持向量机参数优化算法
PREAI
分享收藏M Pathway and Areas 44 and 45 Are Involved in Stereoscopic Recognition Based on Binocular Disparity.
OAAI
OAAI分享收藏Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes支持向量机的特征选择和参数优化: 一种基于特征染色体遗传算法的新方法
PREAI
分享收藏
查看更多内容