NDLI: A new multiresolution classification model based on partitioning of feature space

Content Provider	IEEE Xplore Digital Library
Author	Yinghua He Bo Zhang Jianmin Li
Copyright Year	2005
Description	Author affiliation: State Key Lab of Intelligent Technol. & Syst., Tsinghua Univ., Beijing, China (Yinghua He; Bo Zhang; Jianmin Li)
Abstract	Multiresolution analysis is a hot topic in the past decade. In this paper, we propose a new multiresolution classification method which adopts a coarse-to-fine strategy both during the training and the testing processes based on decomposing of the feature space. The training algorithm locates the boundary between two classes from coarse to fine by dividing the hypercubes which lie on the boundary step by step. The testing algorithm firstly labels the testing data set by the classifier trained at initial resolution. Then, only those lying on the boundary are labeled at the finer resolution. As an example, an approach named MRSVC is proposed, which exploits support vector machines as the basic classifier. Finally, theoretical analysis and experimental results have substantiated the effectiveness of the proposed method.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	462
Ending Page	467
File Size	2321843
Page Count	6
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Space technology Neural networks Support vector machine classification Training data Large-scale systems Helium Intelligent systems Multiresolution analysis Testing
Content Type	Text
Resource Type	Article

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