Multiple task transfer learning with small sample sizes Saha, Budhaditya, Gupta, Sunil, Phung, Dinh and Venkatesh, Svetha 2016, Multiple task transfer learning with small sample sizes, Knowledge and information systems, vol. 46, no. 2, pp. 315-342, doi: 10.1007/s10115-015-0821-z. Attached Files Name Description MIMEType Size Downloads Title Multiple task transfer learning with small sample sizes Author(s) Saha, Budhaditya orcid.org/0000-0001-8011-6801 Gupta, Sunil orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Journal name Knowledge and information systems Volume number 46 Issue number 2 Start page 315 End page 342 Total pages 28 Publisher Springer Place of publication Berlin, Germany Publication date 2016-02 ISSN 0219-1377 0219-3116 Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science Multi-task Transfer learning Optimization Healthcare Data mining Statistical analysis ADAPTATION Summary Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning. Language eng DOI 10.1007/s10115-015-0821-z Field of Research 080109 Pattern Recognition and Data Mining 0801 Artificial Intelligence And Image Processing Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2016, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30076876 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 26 Aug 2015, 14:47:06 EST Thu, 27 Aug 2015, 05:08:04 EST Thu, 27 Aug 2015, 08:51:33 EST Thu, 27 Aug 2015, 08:57:47 EST Mon, 07 Mar 2016, 17:58:31 EST Thu, 10 Mar 2016, 05:22:10 EST Thu, 14 Apr 2016, 09:52:31 EST Thu, 14 Apr 2016, 09:55:29 EST Filtered Full Citation counts:   Cited 9 times in TR Web of Science Cited 12 times in Scopus Search Google Scholar Access Statistics: 666 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 07 Mar 2016, 17:58:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.