机器学习在动物行为分析中应用研究进展
首发时间:2021-04-07
摘要:动物行为分析是研究动物高级神经中枢功能一项重要技术手段,动物的行为及其规律综合反应了其心理和生理状况,在实验动物学领域得到了广泛研究。近年来,行为采集手段的发展让大量行为数据得以产生,机器学习的出现大大提升了动物行为数据的处理效率。其中,监督学习可以实现对动物行为的自动识别和分类,而无监督学习则有利于发现人眼所观测不到的新行为或异常行为,并探索行为结构。本文对机器学习方法在动物行为分析中的应用进行了阐述,为后期的研究工作和方向提供参考。
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Research Progress on Application of Machine Learning in Animal Behavior Analysis
Abstract:The analysis of animal behavior is an important technique to study the function of the central nervous system. Behavior pattern which indicates animal psychological and physical status, has received comprehensive studies in laboratory animal science. Huge amounts of behavior data have been produced because of the development of behavioral collection method, and it is no doubt that machine learning makes data analysis more efficient. Supervised learning is usually used for behavior recognition and classification. Unsupervised learning explores behavior structure, allows for the discovery of novel behaviors and recognition of unusual forms of movements that are not noticeable to human eye.Research Progress on Application of Machine Learning in Animal Behavior Analysis In this paper, we illustrate the application of machine learning in animal behavior analysis, which may provide reference for future researches.
Keywords: Animal Behavior Machine Learning Behavior Recognition Behavior Structure
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