基于抗噪声的多任务多示例学习算法研究

黎启祥; 肖燕珊; 郝志峰; 阮奕邦

doi:10.12052/gdutxb.180036

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基于抗噪声的多任务多示例学习算法研究

综合研究 | 更新时间：2026-05-27

- 基于抗噪声的多任务多示例学习算法研究
- An Algorithm Based on Multi-task Multi-instance Anti-noise Learning
- 广东工业大学学报 2018年35卷第3期页码：47-53
- 作者机构：
  
  1. 广东工业大学　计算机学院,广东,广州,510006
  2. 佛山科学技术学院　数学与大数据学院,广东,佛山,528000
- 作者简介：
  
  黎启祥(1992–)，男，硕士研究生，研究方向为机器学习、数据挖掘、图像处理
  肖燕珊(1981–)，女，教授，博士，主要研究方向为机器学习、图像处理与模式识别. E-mail:xiaoyanshan@189.cn
- 基金信息：
  
  国家自然科学基金资助项目(61472090，61672169，61472089)
- DOI：10.12052/gdutxb.180036
  中图分类号： TP301.6
- 纸质出版：2018
- 稿件说明：
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黎启祥, 肖燕珊, 郝志峰, 阮奕邦. 基于抗噪声的多任务多示例学习算法研究[J]. 广东工业大学学报, 2018, 35(3): 47-53.

Li Qi-xiang, Xiao Yan-shan, Hao Zhi-feng, et al. An Algorithm Based on Multi-task Multi-instance Anti-noise Learning[J]. 2018, 35(3): 47-53.
黎启祥, 肖燕珊, 郝志峰, 阮奕邦. 基于抗噪声的多任务多示例学习算法研究[J]. 广东工业大学学报, 2018, 35(3): 47-53. DOI： 10.12052/gdutxb.180036.

Li Qi-xiang, Xiao Yan-shan, Hao Zhi-feng, et al. An Algorithm Based on Multi-task Multi-instance Anti-noise Learning[J]. 2018, 35(3): 47-53. DOI： 10.12052/gdutxb.180036.

摘要

在多示例学习中

当训练样本数量不充足或者训练样本中存在噪声信息时

分类器的分类性能将降低. 针对该问题

本文提出了一种基于抗噪声的多任务多示例学习算法. 一方面

针对训练样本中可能存在的噪声问题

该算法赋予包中示例不同的权值

通过迭代更新权值来降低噪声数据对预测结果的影响. 另一方面

针对训练样本数量不充足问题

该算法运用多任务学习策略

通过同时训练多个学习任务

利用任务间的关联性来提高各个分类任务的预测性能. 实验结果证明

与现有的分类算法相比

该方法在相同的实验条件下具有更优秀的性能.

Abstract

In multi-instance learning

classification performance may be limited due to the noisy data or a scarce amount of labeled data. To solve this problem

an algorithm based on multi-task multi-instance anti-noise learning is proposed. On the one hand

in view of the noisy data

the algorithm trains a classifier by assigning the instances in bags with different weights. And the weights of instances are updated by adopting an iterative optimization framework which decreases the influence of the noisy data. On the other hand

in view of insufficient labeled data

the classifier is extended to multi-task learning to train multiple learning tasks at the same time

so that the performance of each learning task can be improved by sharing the classification information among the tasks. Extensive experiments have showed that the proposed classification framework outperforms the existing classification methods.

关键词

Keywords

references

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