Recurrent boosting for classification of natural and synthetic time-series data

被引：0

作者：

Vincent, Robert D. ^{[1
,2
]}

Pineau, Joelle ^{[1
]}

de Guzman, Philip ^{[2
]}

Avoli, Massimo ^{[2
]}

机构：

[1] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada

[2] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE | 2007年 / 4509卷

基金：

加拿大自然科学与工程研究理事会; 加拿大健康研究院;

关键词：

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：

Boosted ensemble classifiers have a demonstrated ability to discover regularities in large, poorly modeled datasets. In this paper we present an application of multi-hypothesis AdaBoost to detect epileptiform activity from electrophysiological recordings. While existing boosting methods do not account automatically for the sequence information that is available when analyzing time-series data, we present a recurrent extension to AdaBoost, and show that it improves classification accuracy in our application domain.

引用

页码：192 / +

页数：3

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