Advances in Neural Information Processing Systems 19: by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann

By Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)

The once a year Neural details Processing structures (NIPS) convention is the flagship assembly on neural computation and desktop studying. It attracts a various workforce of attendees—physicists, neuroscientists, mathematicians, statisticians, and laptop scientists—interested in theoretical and utilized facets of modeling, simulating, and construction neural-like or clever platforms. The shows are interdisciplinary, with contributions in algorithms, studying idea, cognitive technological know-how, neuroscience, mind imaging, imaginative and prescient, speech and sign processing, reinforcement studying, and purposes. simply twenty-five percentage of the papers submitted are approved for presentation at NIPS, so the standard is outstandingly excessive. This quantity includes the papers offered on the December 2006 assembly, held in Vancouver.

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Berger, and E. Liang. Autonomous inverted helicopter flight via reinforcement learning. In Int’l Symposium on Experimental Robotics, 2004. [17] Andrew Y. Ng, H. Jin Kim, Michael Jordan, and Shankar Sastry. Autonomous helicopter flight via reinforcement learning. In NIPS 16, 2004. [18] Jonathan M. Roberts, Peter I. Corke, and Gregg Buskey. Low-cost flight control system for a small autonomous helicopter. In IEEE Int’l Conf. on Robotics and Automation, 2003. [19] S. Saripalli, J. F. Montgomery, and G.

Finally, we use the hinge function [a]+ = max{0, a}. Online learning is performed in a sequence of trials. At trial t the algorithm receives a matrix Xt of size kt × n, where each row of Xt is an instance, and is required to make a prediction on the ˆ t . We allow yˆjt label associated with each instance. We denote the vector of predicted labels by y to take any value in R, where the actual label being predicted is sign(ˆ yjt ) and |ˆ yjt | is the confidence ˆ t the algorithm receives the correct labels yt where in the prediction.

We would like to comment that this solution may update αjt also for instances which were correctly classified as long as the margin they attain is not sufficiently large. We abbreviate this scheme as the SimProj algorithm. Conservative Simultaneous Projections: Combining ideas from both methods, the conservative simultaneous projections scheme optimally sets αjt according to the analytic solution. The difference with the SimProj algorithm lies in the selection of μt . In the conservative scheme only the instances which were incorrectly predicted (j ∈ Mt ) are assigned a positive weight.

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