| 摘要: |
| 针对自然人机交互应用中的人体动作识别问题，总结了传统机器学习模型在识别人体动作时的缺点，然后在此基础上针对自然人机交互应用的独特要求提出了面向人体动作识别的随机增量型混合学习机模型.该模型将误差反向传播模型、增量型极限学习机模型和双端增量型极限学习机模型相结合，克服了传统方法在识别人体动作时的不足.详细阐述了针对面向人体动作识别的随机增量型混合学习机模型的算法理论、模型合理性和实现方案.最后通过对比识别实验结果，验证了随机增量型混合学习机模型在识别人体动作问题上具有更好的鲁棒性、实时性和准确性. |
| 关键词: 自然人机交互 机器学习 极限学习机 人体动作识别 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金（61272357，61300074，61572075)；国家重点研发计划（2016YFB0700502，2016YFB1001404) |
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| Random Increased Hybrid Learning Machine Oriented Human Body Movement Identification |
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CHANG Zheng, BAN Xiao-Juan, MA Bo-Yuan, XING Yi-Ming
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School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100086, China
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| Abstract: |
| Focusing on the problem of human movementidentification in the application of natural human-computer interaction, this paper summarizes the shortcomings of the traditional machine learning model in the identification of body movement.Based on the unique requirements of natural human-computer interaction application, it proposesRandom Increased Hybrid Learning Machine for human body movement identification. Combined with the Error Back Propagation Model, the Increased Extreme Learning Machine and Bidirectional Extreme Learning Machine, the model overcomes the shortcomings of traditional methods. This paper describes in detailthe algorithm theory, model rationality and implementation scheme of the Random Increased Hybrid Learning Machine. Finally, by comparing the experimental results, the paper verifies the Random Increased Hybrid Learning Machine's a better robustness, accuracy and timeliness in identification of human body movement. |
| Key words: natural human-computer interaction machine learning extreme learning machine human body movement identification |
