On surrogate supervision multi-view learning Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j098zd482

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  • Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision multi-view learning, where labels are available on limited views and a classifier is obtained on the target view where labels are missing. In surrogate multi-view learning, one cannot obtain a classifier without information from the auxiliary view. To solve this challenging problem, we propose discriminative and generative approaches.
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  • description.provenance : Made available in DSpace on 2013-04-03T21:58:20Z (GMT). No. of bitstreams: 3 license_rdf: 21597 bytes, checksum: 8b74f582a85103e4d783f789d79d319f (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) MS_Thesis.pdf: 445812 bytes, checksum: 1699f1a365c0baf298e01bcc76719ee4 (MD5) Previous issue date: 2012-12-03
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-04-03T21:58:19Z (GMT) No. of bitstreams: 3 license_rdf: 21597 bytes, checksum: 8b74f582a85103e4d783f789d79d319f (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) MS_Thesis.pdf: 445812 bytes, checksum: 1699f1a365c0baf298e01bcc76719ee4 (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-04-03T20:26:27Z (GMT) No. of bitstreams: 3 license_rdf: 21597 bytes, checksum: 8b74f582a85103e4d783f789d79d319f (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) MS_Thesis.pdf: 445812 bytes, checksum: 1699f1a365c0baf298e01bcc76719ee4 (MD5)
  • description.provenance : Submitted by Gaole Jin (jing@onid.orst.edu) on 2013-04-03T20:24:37Z No. of bitstreams: 3 license_rdf: 21597 bytes, checksum: 8b74f582a85103e4d783f789d79d319f (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) MS_Thesis.pdf: 445812 bytes, checksum: 1699f1a365c0baf298e01bcc76719ee4 (MD5)

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