Transition-Aware Human Activity Recognition Using eXtreme Gradient Boosted Decision Trees

被引：18

作者：

Gusain, Kunal ^{[1
]}

Gupta, Aditya ^{[1
]}

Popli, Bhavya ^{[1
]}

机构：

[1] Guru Gobind Singh Indraprastha Univ, Bharati Vidyapeeths Coll Engn, Comp Sci Dept, Delhi, India

来源：

ADVANCED COMPUTING AND COMMUNICATION TECHNOLOGIES | 2018年 / 562卷

关键词：

Machine learning; Gradient boosted machines; XGBoost; Human activity recognition; Transition-Aware;

D O I：

10.1007/978-981-10-4603-2_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gradient Boosted Machines (GBM) have long been used for regression and classification purposes, and their basic premise of boosting weak learners has been adapted and improvised time and time again in search for an optimal modeling approach. One recent variant has taken the world of learning-related problems by storm, by giving the most accurate solutions to most of them, it is called eXtreme GBM. The paper evaluates eXtreme GBM in the context of human activity recognition.

引用

页码：41 / 49

页数：9

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