Skip to main content

Semisupervised multitask learning.

Publication ,  Journal Article
Liu, Q; Liao, X; Carin, HL; Stack, JR; Carin, L
Published in: IEEE transactions on pattern analysis and machine intelligence
June 2009

Context plays an important role when performing classification, and in this paper we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks (multiple distinct data collections). This is referred to as multi-task learning (MTL), and is implemented here in a statistical manner, using a simplified form of the Dirichlet process. In addition, when performing many classification tasks one has simultaneous access to all unlabeled data that must be classified, and therefore there is an opportunity to place the classification of any one feature vector within the context of all unlabeled feature vectors; this is referred to as semi-supervised learning. In this paper we integrate MTL and semi-supervised learning into a single framework, thereby exploiting two forms of contextual information. Example results are presented on a "toy" example, to demonstrate the concept, and the algorithm is also applied to three real data sets.

Duke Scholars

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

June 2009

Volume

31

Issue

6

Start / End Page

1074 / 1086

Related Subject Headings

  • Pattern Recognition, Automated
  • Models, Theoretical
  • Computer Simulation
  • Artificial Intelligence & Image Processing
  • Artificial Intelligence
  • Algorithms
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Liu, Q., Liao, X., Carin, H. L., Stack, J. R., & Carin, L. (2009). Semisupervised multitask learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1074–1086. https://doi.org/10.1109/tpami.2008.296
Liu, Qiuhua, Xuejun Liao, Hui Li Carin, Jason R. Stack, and Lawrence Carin. “Semisupervised multitask learning.IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 6 (June 2009): 1074–86. https://doi.org/10.1109/tpami.2008.296.
Liu Q, Liao X, Carin HL, Stack JR, Carin L. Semisupervised multitask learning. IEEE transactions on pattern analysis and machine intelligence. 2009 Jun;31(6):1074–86.
Liu, Qiuhua, et al. “Semisupervised multitask learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, June 2009, pp. 1074–86. Epmc, doi:10.1109/tpami.2008.296.
Liu Q, Liao X, Carin HL, Stack JR, Carin L. Semisupervised multitask learning. IEEE transactions on pattern analysis and machine intelligence. 2009 Jun;31(6):1074–1086.

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

June 2009

Volume

31

Issue

6

Start / End Page

1074 / 1086

Related Subject Headings

  • Pattern Recognition, Automated
  • Models, Theoretical
  • Computer Simulation
  • Artificial Intelligence & Image Processing
  • Artificial Intelligence
  • Algorithms
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems