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

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

The yearly Neural info Processing platforms (NIPS) convention is the flagship assembly on neural computation and computing device studying. It attracts a various crew of attendees—physicists, neuroscientists, mathematicians, statisticians, and laptop scientists—interested in theoretical and utilized points of modeling, simulating, and development neural-like or clever platforms. The displays are interdisciplinary, with contributions in algorithms, studying thought, cognitive technological know-how, neuroscience, mind imaging, imaginative and prescient, speech and sign processing, reinforcement studying, and purposes. in basic terms twenty-five percentage of the papers submitted are approved for presentation at NIPS, so the standard is outstandingly excessive. This quantity includes the papers provided on the December 2006 assembly, held in Vancouver.

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19] S. Saripalli, J. F. Montgomery, and G. S. Sukhatme. Visually-guided landing of an unmanned aerial vehicle. IEEE Transactions on Robotics and Autonomous Systems, 2003. [20] J. Seddon. Basic Helicopter Aerodynamics. AIAA Education Series. America Institute of Aeronautics and Astronautics, 1990. 8 Tighter PAC-Bayes Bounds Amiran Ambroladze Dep. se Emilio Parrado-Hern´andez Dep. es John Shawe-Taylor Dep. uk Abstract This paper proposes a PAC-Bayes bound to measure the performance of Support Vector Machine (SVM) classifiers.

2, (see [4]); Pr(x,y)∼D sign(wT φ(x)) = y ≤ 2QD (w, μ) for all μ. 3 Choosing a prior for the PAC-Bayes Bound Our first contribution is motivated by the fact that the PAC-Bayes bound allows us to choose the prior distribution, P (c). In the standard application of the bound this is chosen to be a Gaussian centred at the origin. We now consider learning a different prior based on training an SVM on a subset R of the training set comprising r training patterns and labels. In the experiments this is taken as a random subset but for simplicity of the presentation we will assume these to be the last r examples {xk , yk }m k=m−r+1 in the description below.

Such an approach was used for instance by Weston and Watkins [1] for batch learning of multiclass support vector machines. 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 [2]). 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.

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