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Complexity-constrained feature selection for classification: 2007 DIGEST OF TECHNICAL PAPERS INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2007 (English)In: IEEE ICCE, 2007, 9-10 p.Conference paper, Published paper (Refereed)
Abstract [en]

Continuous monitoring of audio-visual context on mobile devices requires algorithms with gentle demands on computational resources. Existing feature selection strategies for classification do not account for the complexity associated with feature extraction. We present a complexity-constrained feature selection algorithm that is independent of the classifier architecture and demonstrate that it leads to superior feature sets if the allowed computational complexity is limited.

Place, publisher, year, edition, pages
2007. 9-10 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-36128DOI: 10.1109/ICCE.2007.341371ISI: 000245430200005Scopus ID: 2-s2.0-34548026057OAI: oai:DiVA.org:kth-36128DiVA: diva2:430504
Conference
25th IEEE International Conference on Consumer Electronics Las Vegas, NV, JAN 10-14, 2007
Note
QC 20110711Available from: 2011-07-11 Created: 2011-07-08 Last updated: 2011-07-11Bibliographically approved

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