Negative results for active learning with convex losses

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 321-325

  Hanneke, Steve ^a   Yang, Liu ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; BOUNDED NOISE; LOSS FUNCTIONS; MINIMAX; PASSIVE LEARNING;

SOFTWARE ENGINEERING;

ARTIFICIAL INTELLIGENCE;

EID: 84862288088     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (12)

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4. Beygelzimer, A., Dasgupta, S., and Langford, J. (2009). Importance weighted active learning.
5. Castro, R. and Nowak, R. (2006). Upper and lower error bounds for active learning. In The 44th Annual Allerton Conference on Communication, Control and Computing.
6. Dasgupta, S. (2005). Coarse sample complexity bounds for active learning. In Advances in Neural Information Processing Systems 18.
7. Dasgupta, S., Hsu, D., and Monteleoni, C. (2007). A general agnostic active learning algorithm. Technical Report CS2007-0898, Department of Computer Science and Engineering, University of California, San Diego.
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11. Hanneke, S. (2009b). Theoretical Foundations of Active Learning. Ph.D. thesis, Machine Learning Department, School of Computer Science, Carnegie Mellon University.
12. Tong, S. and Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2.