Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/78929
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dc.contributor.author | Shen, C. | - |
dc.contributor.author | Li, H. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Neural Networks, 2013; 48:44-58 | - |
dc.identifier.issn | 0893-6080 | - |
dc.identifier.issn | 1879-2782 | - |
dc.identifier.uri | http://hdl.handle.net/2440/78929 | - |
dc.description.abstract | We propose a general framework for analyzing and developing fully corrective boosting-based classifiers. The framework accepts any convex objective function, and allows any convex (for example, lp-norm, p ≥ 1) regularization term. By placing the wide variety of existing fully corrective boosting-based classifiers on a common footing, and considering the primal and dual problems together, the framework allows direct com- parison between apparently disparate methods. By solving the primal rather than the dual the framework is capable of generating efficient fully-corrective boosting algorithms without recourse to sophisticated convex optimization processes. We show that a range of additional boosting-based algorithms can be incorporated into the framework despite not being fully corrective. Finally, we provide an empirical analysis of the per- formance of a variety of the most significant boosting-based classifiers on a few machine learning benchmark datasets. | - |
dc.description.statementofresponsibility | Chunhua Shen, Hanxi Li, Anton van den Hengel | - |
dc.language.iso | en | - |
dc.publisher | Pergamon-Elsevier Science Ltd | - |
dc.rights | Copyright © 2013 Elsevier Ltd. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.neunet.2013.07.006 | - |
dc.subject | Boosting | - |
dc.subject | ensemble learning | - |
dc.subject | convex optimization | - |
dc.subject | column generation | - |
dc.title | Fully corrective boosting with arbitrary loss and regularization | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.neunet.2013.07.006 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FT120100969 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FT120100969 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_78929.pdf | Accepted version | 549.63 kB | Adobe PDF | View/Open |
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