Anytime learning of anycost classifiers

Author:

Esmeir Saher,Markovitch Shaul

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference60 articles.

1. Abe, N., Zadrozny, B., & Langford, J. (2004). An iterative method for multi-class cost-sensitive learning. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2004), Seattle, WA, USA (pp. 3–11).

2. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://www.ics.uci.edu/~mlearn/MLRepository.html .

3. Baram, Y., El-Yaniv, R., & Luz, K. (2003). Online choice of active learning algorithms. In Proceedings of the 20th international conference on machine learning (ICML-2003), Washington, DC, USA (pp. 19–26).

4. Bayer-Zubek, V., & Dietterich, T.G. (2005). Integrating learning from examples into the search for diagnostic policies. Artificial Intelligence, 24(1), 263–303.

5. Bilgic, M., & Getoor, L. (2007). Voila: Efficient feature-value acquisition for classification. In Proceedings of the 22nd national conference on artificial intelligence (AAAI-2007), Vancouver, British Columbia, Canada (pp. 1225–1230).

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