Active learning for probabilistic hypotheses using the maximum Gibbs error criterion

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Nguyen, Viet Cuong ^a   Lee, Wee Sun ^a   Ye, Nan ^a   Chai, Kian Ming A ^b   Chieu, Hai Leong ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b DSO NATIONAL LABORATORIES   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

TEXT PROCESSING;

BAYESIAN CONDITIONAL RANDOM FIELDS; EXPECTED ERROR RATES; GREEDY STRATEGIES; LABEL DISTRIBUTION; NAMED ENTITY RECOGNITION; OBJECTIVE FUNCTIONS; PRIOR DISTRIBUTION; TEXT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84898957551     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Andrew McCallum and Kamal Nigam. Employing EM and Pool-Based Active Learning for Text Classification. In International Conference on Machine Learning (ICML), pages 350-358, 1998.
2. Daniel Golovin and Andreas Krause. Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. Journal of Artificial Intelligence Research, 42(1):427-486, 2011.
3. Yuxin Chen and Andreas Krause. Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization. In International Conference on Machine Learning (ICML), pages 160-168, 2013.
4. Constantino Tsallis and Edgardo Brigatti. Nonextensive statistical mechanics: A brief introduction. Continuum Mechanics and Thermodynamics, 16(3):223-235, 2004.
5. Steven CH Hoi, Rong Jin, Jianke Zhu, and Michael R Lyu. Batch Mode Active Learning and Its Application to Medical Image Classification. In International Conference on Machine learning (ICML), pages 417-424. ACM, 2006.
6. John Lafferty, Andrew McCallum, and Fernando CN Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In International Conference on Machine Learning (ICML), pages 282-289, 2001.
7. Bassem Sayrafi, Dirk Van Gucht, and Marc Gyssens. The implication problem for measure-based constraints. Information Systems, 33(2):221-239, 2008.
8. G.L. Nemhauser and L.A. Wolsey. Best Algorithms for Approximating the Maximum of a Submodular Set Function. Mathematics of Operations Research, 3(3):177-188, 1978.
9. Sunita Sarawagi and William W. Cohen. Semi-Markov Conditional Random Fields for Information Extraction. Advances in Neural Information Processing Systems (NIPS), 17:1185-1192, 2004.
10. Viet Cuong Nguyen, Nan Ye, Wee Sun Lee, and Hai Leong Chieu. Semi-Markov Conditional Random Field with High-Order Features. In ICML Workshop on Structured Sparsity: Learning and Inference, 2011.
11. Erik F. Tjong Kim Sang and Fien De Meulder. Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition. In Proceedings of the 17th Conference on Natural Language Learning (HLT-NAACL 2003), pages 142-147, 2003.
12. Burr Settles and Mark Craven. An Analysis of Active Learning Strategies for Sequence Labeling Tasks. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1070-1079. Association for Computational Linguistics, 2008.
13. Thorsten Joachims. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Technical report, DTIC Document, 1996.
14. Burr Settles. Active Learning Literature Survey. Technical Report 1648, University of Wisconsin-Madison, 2009.
15. Robert Nowak. Noisy Generalized Binary Search. Advances in Neural Information Processing Systems (NIPS), 22:1366-1374, 2009.
16. Daniel Golovin, Andreas Krause, and Debajyoti Ray. Near-Optimal Bayesian Active Learning with Noisy Observations. In Advances in Neural Information Processing Systems (NIPS), pages 766-774, 2010.