特征增量极限学习机

计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 112-116.

特征增量极限学习机

赵中堂^1,2, 郑小东¹

(郑州航空工业管理学院智能工程学院郑州450046)¹;
(北京理工大学智能机器人与系统高精尖创新中心北京100081)²

出版日期:2019-11-10 发布日期:2019-11-20
通讯作者: 赵中堂(1978－),男,博士,副教授,CCF会员,主要研究方向为机器学习,E-mail:112864041@qq.com。
基金资助:
本文受国家自然科学基金项目(U1504609),北京理工大学智能机器人与系统高精尖创新中心开放基金项目(2018IRS09),河南省2017科技发展计划项目(172102210525),河南省高等学校青年骨干教师培养计划项目(2017GGJS111),河南省高等教育教学改革研究与实践一般项目(2017SJGLX400)资助。

Feature Incremental Extreme Learning Machine

ZHAO Zhong-tang^1,2, ZHENG Xiao-dong¹

(School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China)¹;
(Beijing Advanced Innovation Center for Intelligent Robots and Systems,Beijing Institute of Technology,Beijing 100081,China)²

Online:2019-11-10 Published:2019-11-20

摘要/Abstract

摘要： 在机器学习的不同应用领域,出现了很多优秀的极限学习机分类模型。研究者往往愿意公开这些模型的结构以及参数,但不愿公开原始训练数据。针对如何仅利用现有的模型和少量具有新特征的样本得到一个更高效的识别模型的问题,提出一种特征增量极限学习机算法。该算法能从具有新特征的样本中学习知识,提高现有模型的识别精度。在真实世界图像和三轴加速度传感器数据集上的测试结果表明,该算法能有效地工作,在不需要以往训练样本参与的情况下,能一定程度上提高已有模型的识别精度,得到新的识别模型。

关键词: 机器学习, 普适计算, 迁移学习, 增量学习

Abstract: In different application fields of machine learning,many excellent classification models of extreme learning machine were produced.Researchers are often willing to share the structure and parameters of these models,but are reluctant to share the original training data.To solve the problem of how to use a small number of samples with new features and the existing classifier to generate a more efficient classifier,this paper proposed a feature incremental extreme learning machine,which can learn knowledge from samples with new features and improve the recognition accuracy of existing models.Test results on real world datasets show that the proposed algorithm can work effectively and improve the recognition accuracy of existing models,without the participation of previous training samples.

Key words: Incremental learning, Machine learning, Pervasive computing, Transfer learning

中图分类号:

TP393

赵中堂, 郑小东. 特征增量极限学习机[J]. 计算机科学, 2019, 46(11A): 112-116. https://doi.org/

ZHAO Zhong-tang, ZHENG Xiao-dong. Feature Incremental Extreme Learning Machine[J]. Computer Science, 2019, 46(11A): 112-116. https://doi.org/

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