By Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)
The once a year Neural details Processing platforms (NIPS) convention is the flagship assembly on neural computation and computer studying. It attracts a various crew of attendees—physicists, neuroscientists, mathematicians, statisticians, and machine scientists—interested in theoretical and utilized facets of modeling, simulating, and construction neural-like or clever structures. The displays are interdisciplinary, with contributions in algorithms, studying thought, cognitive technology, neuroscience, mind imaging, imaginative and prescient, speech and sign processing, reinforcement studying, and purposes. basically twenty-five percentage of the papers submitted are authorized for presentation at NIPS, so the standard is phenomenally excessive. This quantity comprises the papers awarded on the December 2006 assembly, held in Vancouver.
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Extra resources for Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
In the ﬁrst phase, the algorithms solve simultaneously multiple sub-problems. Each sub-problem distills to an optimization problem with a single linear constraint from the original multiple-constraints problem. The simple structure of each single-constraint problem results in an analytical solution which is efﬁciently computable. In the second phase, the algorithms take a convex combination of the independent solutions to obtain a solution for the multiple-constraints problem. The end result is an approach whose time complexity and mistake bounds are equivalent to approaches which solely deal with the worst-violating constraint .
Singer. Online passive aggressive algorithms. Journal of Machine Learning Research, 7, Mar 2006.  M. Fink, S. Shalev-Shwartz, Y. Singer, and S. Ullman. Online multiclass learning by interclass hypothesis sharing. In Proc. of the 23rd International Conference on Machine Learning, 2006.  N. Littlestone. Learning when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285–318, 1988.  J. Kivinen and M. Warmuth. Exponentiated gradient versus gradient descent for linear predictors.
The simplicity of this approach also underscores its deﬁciency as it is detached from the original loss of the complex decision problem. The second approach maintains the original structure of the problem but focuses on a single, worst performing, derived sub-problem (see for instance ). While this approach adheres with the original structure of the problem, the resulting update mechanism is by construction sub-optimal as it oversees almost all of the constraints imposed by the complex prediction problem.
Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)