NDLI: Analysis of machine learning techniques for context extraction

Content Provider	IEEE Xplore Digital Library
Author	Granitzer, M. Kroll, M. Seifert, C. Rath, A.S. Weber, N. Dietzel, O. Lindstaedt, S.
Copyright Year	2008
Description	Author affiliation: Knowledge Manage. Inst., Graz Univ. of Technol., Graz (Granitzer, M.; Kroll, M.) \|\| M2N Consulting & Dev. GmbH (Dietzel, O.) \|\| Know-Center Graz, Graz (Seifert, C.; Rath, A.S.; Weber, N.; Lindstaedt, S.)
Abstract	dasiaContext is keypsila conveys the importance of capturing the digital environment of a knowledge worker. Knowing the userpsilas context offers various possibilities for support, like for example enhancing information delivery or providing work guidance. Hence, user interactions have to be aggregated and mapped to predefined task categories. Without machine learning tools, such an assignment has to be done manually. The identification of suitable machine learning algorithms is necessary in order to ensure accurate and timely classification of the userpsilas context without inducing additional workload. This paper provides a methodology for recording user interactions and an analysis of supervised classification models, feature types and feature selection for automatically detecting the current task and context of a user. Our analysis is based on a real world data set and shows the applicability of machine learning techniques.
Starting Page	233
Ending Page	240
File Size	149564
Page Count	8
File Format	PDF
ISBN	9781424429165
DOI	10.1109/ICDIM.2008.4746809
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Support vector machines Machine learning algorithms Support vector machine classification Machine learning Information retrieval Knowledge management Mutual information Pattern matching Context modeling
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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