By Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)
ISBN-10: 0262195682
ISBN-13: 9780262195683
ISBN-10: 0262256916
ISBN-13: 9780262256919
The yearly Neural details Processing structures (NIPS) convention is the flagship assembly on neural computation and computing device studying. It attracts a various workforce of attendees—physicists, neuroscientists, mathematicians, statisticians, and machine scientists—interested in theoretical and utilized features of modeling, simulating, and development neural-like or clever platforms. The shows are interdisciplinary, with contributions in algorithms, studying conception, cognitive technology, neuroscience, mind imaging, imaginative and prescient, speech and sign processing, reinforcement studying, and purposes. simply twenty-five percentage of the papers submitted are authorized for presentation at NIPS, so the standard is phenomenally excessive. This quantity includes the papers provided on the December 2006 assembly, held in Vancouver.
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Singh. Near-optimal reinforcement learning in polynomial time. Machine Learning Journal, 2002. [13] M. La Civita, G. Papageorgiou, W. C. Messner, and T. Kanade. Design and flight testing of a highbandwidth H∞ loop shaping controller for a robotic helicopter. Journal of Guidance, Control, and Dynamics, 29(2):485–494, March-April 2006. [14] J. Leishman. Principles of Helicopter Aerodynamics. Cambridge University Press, 2000. [15] B. Mettler, M. Tischler, and T. Kanade. System identification of small-size unmanned helicopter dynamics.
We show that this approach constitutes a feasible, albeit not necessarily optimal, solution for the original projection problem. We derive concrete simultaneous projection schemes and analyze them in the mistake bound model. We demonstrate the power of the proposed algorithm in experiments with online multiclass text categorization. Our experiments indicate that a combination of class-dependent features with the simultaneous projection method outperforms previously studied algorithms. 1 Introduction In this paper we discuss and analyze a framework for devising efficient online learning algorithms for complex prediction problems such as multiclass categorization.
Kearns and D. Koller. Efficient reinforcement learning in factored MDPs. In Proc. IJCAI, 1999. [12] M. Kearns and S. Singh. Near-optimal reinforcement learning in polynomial time. Machine Learning Journal, 2002. [13] M. La Civita, G. Papageorgiou, W. C. Messner, and T. Kanade. Design and flight testing of a highbandwidth H∞ loop shaping controller for a robotic helicopter. Journal of Guidance, Control, and Dynamics, 29(2):485–494, March-April 2006. [14] J. Leishman. Principles of Helicopter Aerodynamics.
Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)
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