Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/67027
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Shen, C. | - |
dc.contributor.author | Wang, P. | - |
dc.contributor.author | Shen, F. | - |
dc.contributor.author | Wang, H. | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012; 34(4):825-832 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.issn | 1939-3539 | - |
dc.identifier.uri | http://hdl.handle.net/2440/67027 | - |
dc.description.abstract | It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik’s alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model’s capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone. | - |
dc.description.statementofresponsibility | Chunhua Shen, Peng Wang, Fumin Shen and Hanzi Wang | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Soc | - |
dc.rights | Copyright 2012 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/tpami.2011.240 | - |
dc.subject | Universum | - |
dc.subject | kernel methods | - |
dc.subject | boosting | - |
dc.subject | column generation | - |
dc.subject | convex optimization | - |
dc.title | UBoost: Boosting with the Universum | - |
dc.title.alternative | {cal U}Boost: Boosting with the Universum | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TPAMI.2011.240 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shen, C. [0000-0002-8648-8718] | - |
Appears in Collections: | Aurora harvest Computer Science publications |
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File | Description | Size | Format | |
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hdl_67027.pdf | Accepted version | 481.95 kB | Adobe PDF | View/Open |
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