Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming

Lubin YU; Qiliang DU; Lianfang TIAN

doi:10.11999/JEIT190473

Volume 42 Issue 11

Nov. 2020

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Lubin YU, Qiliang DU, Lianfang TIAN. Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2742-2748. doi: 10.11999/JEIT190473

Citation:

Lubin YU, Qiliang DU, Lianfang TIAN. Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2742-2748. doi: 10.11999/JEIT190473

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Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming

doi: 10.11999/JEIT190473

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)

Received Date: 2019-06-27
Rev Recd Date: 2020-04-19

Available Online: 2020-08-31

Publish Date: 2020-11-16

Abstract

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.
- Object detection,
- Adaboost algorithm,
- Fast traing,
- Adaptive,
- Weight distribution

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References(15)

References

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