杜启亮,,
田联房
华南理工大学自动化科学与工程学院 广州 510640
基金项目:海防公益类项目(201505002)，广东省重点研发计划-新一代人工智能(20180109)，广州市产业技术重大攻关计划(2019-01-01-12-1006-0001)，广东省科学技术厅重大科技计划项目(2016B090912001)，中央高校基本科研业务费专项资金(2018KZ05)
详细信息
作者简介:余陆斌：男，1994年生，博士生，主要研究方向为机器学习、机器视觉
杜启亮：男，1980年生，副研究员，博士，主要研究方向为机器人、机器视觉
田联房：男，1968年生，教授，博士，主要研究方向为模式识别、人工智能
通讯作者:杜启亮 qldu@scut.edu.cn
中图分类号:TP391计量
文章访问数:521
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被引次数:0
出版历程
收稿日期:2019-06-27
修回日期:2020-04-19
网络出版日期:2020-08-31
刊出日期:2020-11-16
Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming
Lubin YU,Qiliang DU,,
Lianfang TIAN
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Funds:The Coast defence Public Welfare Project (201505002), Guangdong Province Key R&D Program-A New Generation of Artificial Intelligence (20180109), Guangzhou City Industrial Technology Major Research Project (2019-01-01-12-1006-0001), The Major Science and Technology Plan Project of Guangdong Science and Technology Department (2016B090912001), The Special Fund for Basic Scientific Research in Central Colleges and Universities (2018KZ05)
摘要
摘要:Adaboost是一种广泛使用的机器学习算法，然而Adaboost算法在训练时耗时十分严重。针对该问题，该文提出一种基于自适应权值的Adaboost快速训练算法AWTAdaboost。该算法首先统计每一轮迭代的样本权值分布，再结合当前样本权值的最大值和样本集规模计算出裁剪系数，权值小于裁剪系数的样本将不参与训练，进而加快了训练速度。在INRIA数据集和自定义数据集上的实验表明，该文算法能在保证检测效果的情况下大幅加快训练速度，相比于其他快速训练算法，在训练时间接近的情况下有更好的检测效果。
关键词:目标检测/
Adaboost算法/
快速训练/
自适应/
权值分布
Abstract:The Adaboost algorithm provides noteworthy benefits over the traditional machine algorithms for numerous applications, including face recognition, text recognition, and pedestrian detection. However, it takes a lot of time during the training process that affects the overall performance. Adaboost fast training algorithm based on adaptive weight (Adaptable Weight Trimming Adaboost, AWTAdaboost) is proposed in this work to address the aforementioned issue. First, the algorithm counts the current sample weight distribution of each iteration. Then, it combines the maximum value of current sample weights with data size to calculate the adaptable coefficients. The sample whose weight is less than the adaptable coefficients is discarded, that speeds up the training. The experimental results validate that it can significantly speed up the training speed while ensuring the detection effect. Compared with other fast training algorithms, the detection effect is better when the training time is close to each other.
Key words:Object detection/
Adaboost algorithm/
Fast traing/
Adaptive/
Weight distribution
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