NDLI: Tracking concept drifting with an online-optimized incremental learning framework

Content Provider	ACM Digital Library
Author	Ding, Dayong Hua, Xian-Sheng Wu, Jun Zhang, Bo
Abstract	Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series such as video streams over a relatively long period of time. An Online-Optimized Incremental Learning framework is proposed as an example learning system for tracking the drifting concepts. Furthermore, a set of measures are defined to track the process of concept drifting in the learning system. These tracking measures are also applied to determine the corresponding parameters used for model updating in order to obtain the optimal up-to-date classifiers. Experiments on the data set of TREC Video Retrieval Evaluation 2004 not only demonstrate the inside concept drifting process of the learning system, but also prove that the proposed learning framework is promising for tackling the issue of concept drifting.
Starting Page	33
Ending Page	40
Page Count	8
File Format	PDF
ISBN	1595932445
DOI	10.1145/1101826.1101834
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-11-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Incremental learning Video content analysis Trec video retrieval evaluation Gaussian mixture model Concept drifting
Content Type	Text
Resource Type	Article

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Tracking concept drifting with an online-optimized incremental learning framework