A comparison of two machine-learning techniques to focus the diagnosis task

被引：0

作者：

Prieto, Oscar ^{[1
]}

Bregon, Anibal ^{[1
]}

机构：

[1] Univ Valladolid, Dept Comp Sci, Intelligent Syst Grp GSI, ETSI Informat, Campus Miguel Delibes S-N, E-47011 Valladolid, Spain

来源：

STAIRS 2006 | 2006年 / 142卷

关键词：

Machine-learning; Boosting; Dynamic Time Warping;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work considers a time series classification task: fault identification in dynamic systems. Two methods are compared: i) Boosting and ii) K-Nearest Neighbors with Dynamic Time Warping distance.

引用

页码：265 / +

页数：2

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