利用机器学习预测兽脚亚目恐龙狩猎策略。

Matthew Millar
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引用次数: 1

摘要

由于大数据和更好的数据收集粒度,在不同领域使用机器学习正在成为一种更普遍的做法。由于可以收集的数据的大小、类型和质量的增加,机器学习在动物行为模式分析中的应用正变得越来越受欢迎。机器学习甚至可以根据某些特征来预测动物的实际行为。这种方法也可用于预测灭绝动物的行为。这篇论文的目标是探索使用机器学习技术来预测恐龙捕食习惯的可能性,这仅仅是基于动物的身体特征。通过使用生物力学特征,可以创建一个模型来帮助将动物分类为食腐动物或猎人角色。测试结果表明,身体特征与潜在的狩猎习惯之间存在很强的相关性。这里使用的模型可以作为预测其他兽脚亚目恐龙的良好基线,仅基于它们的身体。霸王龙被用作测试对象,在大多数模型中被正确地归类为主要猎人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Theropod Hunting Tactics using Machine Learning.
The use of machine learning in different fields is becoming a more common practice thanks to Big Data and better granularity in data being collected. The application of machine learning to animal behavioral pattern analysis is becoming more popular due to the increase in size, types, and quality of data that can be gathered. Machine learning can even be used to predict the actual behavior of animals based off of certain features. This approach can also be used for predicting the behavior of extinct animals. This paper is the goal is to explore the possibility of using machine learning techniques to predict the hunting habits of dinosaurs based solely off of physical characteristic of the animal. By using the biomechanical features, a model can be created to aid in the classification of animals into either a scavenger or hunter roles. The results from the test show that there is a strong correlation between the physical characteristics and potential hunting habits. The models used here can then be used as a good baseline in predicting other theropods based solely on their bodies. The T-Rex was used as the test subject and was correctly classified as a primary hunter in most of the models.
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