NDLI: Multi-instance learning with relational information of instances

Content Provider	IEEE Xplore Digital Library
Author	Herman, G. Getian Ye Yang Wang Jie Xu Bang Zhang
Copyright Year	2009
Description	Author affiliation: National ICT Australia (NICTA) and UNSW, Sydney, Australia (Herman, G.; Getian Ye; Yang Wang; Jie Xu; Bang Zhang)
Abstract	Multi-instance learning (MIL) has many applications, including image and text categorization. One of the most effective approaches to MIL is by using Support Vector Machines with multi-instance kernels. In this paper we propose a multi-instance kernel, called MIR-Kernel, that takes into account the relational information of instances when computing similarities between bags. The relational information of instances are derived from the statistics of the distances between instances in feature space. The aim of MIR-Kernel is to efficiently capture the context in which instances occur within bags, so that it is able to better compute the similarities between bags. Experimental results on image and text categorization demonstrate the effectiveness of the proposed method compared to other methods.
Starting Page	1
Ending Page	7
File Size	621639
Page Count	7
File Format	PDF
ISBN	9781424454976
ISSN	15505790
e-ISBN	9781424454983
DOI	10.1109/WACV.2009.5403078
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Text categorization Statistics Australia Image recognition Text recognition Drugs Image retrieval Object recognition
Content Type	Text
Resource Type	Article

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