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http://bura.brunel.ac.uk/handle/2438/9949
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DC Field | Value | Language |
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dc.contributor.author | Farquhar, J | - |
dc.contributor.author | Hardoon, DR | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Shawe-Taylor, J | - |
dc.contributor.author | Szedmák, S | - |
dc.date.accessioned | 2015-01-27T11:05:52Z | - |
dc.date.available | 2005 | - |
dc.date.available | 2015-01-27T11:05:52Z | - |
dc.date.issued | 2005 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/9949 | - |
dc.description.abstract | Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When two views of the same phenomenon are available kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve the performance of classification algorithms such as the Support Vector Machine (SVM). This paper takes this observation to its logical conclusion and proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K. We present both experimental and theoretical analysis of the approach showing encouraging results and insights. | en_US |
dc.language.iso | en | en_US |
dc.publisher | School of Electronics and Computer Science, University of Southampton, Southampton, England, 2005 | en_US |
dc.source | Advances in Neural Information Processing Systems (NIPS) | - |
dc.subject | High dimensional feature spaces | en_US |
dc.subject | Relevant subspaces | en_US |
dc.subject | Canonical Correlation Analysis (KCCA) | en_US |
dc.title | Two view learning: SVM-2K, theory and practice | en_US |
dc.type | Article | en_US |
pubs.publication-status | Published | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering/Electronic and Computer Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies/Biomedical Engineering and Healthcare Technologies | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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Fulltext.pdf | 103.55 kB | Adobe PDF | View/Open |
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