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
收藏
页码：265 / +
页数：2
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