基于自适应权值裁剪的Adaboost快速训练算法  

Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming

作　　者：余陆斌 杜启亮[1] 田联房[1] YU Lubin;DU Qiliang;TIAN Lianfang(College of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]华南理工大学自动化科学与工程学院,广州510640

出　　处：《电子与信息学报》2020年第11期2742-2748,共7页Journal of Electronics & Information Technology

基　　金：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)。

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。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.

关 键 词：目标检测 ADABOOST算法 快速训练 自适应 权值分布 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]