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ALT
2006
Springer
16 years 3 months ago
Risk-Sensitive Online Learning
We consider the problem of online learning in settings in which we want to compete not simply with the rewards of the best expert or stock, but with the best trade-off between rew...
Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman
SIGIR
2009
ACM
16 years 1 months ago
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval
In this paper, we propose a Bayesian learning approach to promoting diversity for information retrieval in biomedicine and a re-ranking model to improve retrieval performance in t...
Xiangji Huang, Qinmin Hu
COLT
1992
Springer
15 years 11 months ago
Language Learning from Stochastic Input
Language learning from positive data in the Gold model of inductive inference is investigated in a setting where the data can be modeled as a stochastic process. Specifically, the...
Shyam Kapur, Gianfranco Bilardi
ECML
2004
Springer
15 years 10 months ago
Efficient Hyperkernel Learning Using Second-Order Cone Programming
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
Ivor W. Tsang, James T. Kwok
ECML
2006
Springer
15 years 10 months ago
Cost-Sensitive Learning of SVM for Ranking
Abstract. In this paper, we propose a new method for learning to rank. `Ranking SVM' is a method for performing the task. It formulizes the problem as that of binary classific...
Jun Xu, Yunbo Cao, Hang Li, Yalou Huang