Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105691
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Jiang, W | en_US |
dc.creator | Deng, C | en_US |
dc.creator | Liu, W | en_US |
dc.creator | Nie, F | en_US |
dc.creator | Chung, FL | en_US |
dc.creator | Huang, H | en_US |
dc.date.accessioned | 2024-04-15T07:35:55Z | - |
dc.date.available | 2024-04-15T07:35:55Z | - |
dc.identifier.isbn | 978-0-9992411-0-3 (Online) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105691 | - |
dc.language.iso | en | en_US |
dc.publisher | International Joint Conferences on Artificial Intelligence | en_US |
dc.rights | Copyright © 2017 International Joint Conferences on Artificial Intelligence | en_US |
dc.rights | All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. | en_US |
dc.rights | Posted with permission of the IJCAI Organization (https://www.ijcai.org/). | en_US |
dc.rights | The following publication Jiang, W., Deng, C., Liu, W., Nie, F., Chung, F. L., & Huang, H. (2017). Theoretic analysis and extremely easy algorithms for domain adaptive feature learning. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017, p. 1958-1964 is available at https://doi.org/10.24963/ijcai.2017/272. | en_US |
dc.title | Theoretic analysis and extremely easy algorithms for domain adaptive feature learning | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 1958 | en_US |
dc.identifier.epage | 1964 | en_US |
dc.identifier.doi | 10.24963/ijcai.2017/272 | en_US |
dcterms.abstract | Domain adaptation problems arise in a variety of applications, where a training dataset from the source domain and a test dataset from the target domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to another feature learning algorithm. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on Amazon review and spam datasets from the ECML/PKDD 2006 discovery challenge. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017, p. 1958-1964 | en_US |
dcterms.issued | 2017 | - |
dc.relation.conference | International Joint Conference on Artificial Intelligence [IJCAI] | en_US |
dc.description.validate | 202402 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | COMP-1368 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | NSF | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9605792 | - |
dc.description.oaCategory | Publisher permission | en_US |
Appears in Collections: | Conference Paper |
Page views
12
Citations as of May 12, 2024
Downloads
1
Citations as of May 12, 2024
SCOPUSTM
Citations
5
Citations as of May 17, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.