A Potential-based Framework for Online Multi-class Learning with Partial Feedback
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[1] John N. Tsitsiklis,et al. The Sample Complexity of Exploration in the Multi-Armed Bandit Problem , 2004, J. Mach. Learn. Res..
[2] Dale Schuurmans,et al. General Convergence Results for Linear Discriminant Updates , 1997, COLT '97.
[3] Koby Crammer,et al. Ultraconservative Online Algorithms for Multiclass Problems , 2001, J. Mach. Learn. Res..
[4] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[5] Shie Mannor,et al. PAC Bounds for Multi-armed Bandit and Markov Decision Processes , 2002, COLT.
[6] J. Langford,et al. The Epoch-Greedy algorithm for contextual multi-armed bandits , 2007, NIPS 2007.
[7] Peter Auer,et al. The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..
[8] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[9] Gábor Lugosi,et al. Prediction, learning, and games , 2006 .
[10] Shie Mannor,et al. Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems , 2006, J. Mach. Learn. Res..
[11] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[12] Manfred K. Warmuth,et al. Additive versus exponentiated gradient updates for linear prediction , 1995, STOC '95.
[13] Ambuj Tewari,et al. Efficient bandit algorithms for online multiclass prediction , 2008, ICML '08.