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Gaussian Processes for Classification

Speaker: Dr. Amir Atiya
Cairo University, Egypt (currently on leave at Veros Systems, TX)

February 02, 2012 - 4:00 p.m. to 5:00 p.m.
Location: Troy 2012
Hosted By: Dr. Malik Magdon-Ismail (x4857)


The Gaussian process classifier (GPC) is a very promising novel machine learning concept that is based on a probabilistic model of the underlying class probabilities. The model is based on a Bayesian formulation that combines the observed class memberships with an assumed prior distribution. While its counterpart, the Gaussian process regression (GPR), has a simple closed-form solution and therefore enjoys wide applicability, the GPC has some computational issues to overcome. It is given in terms of a prohibitive multi-integral formula, that can only be solved through some approximations or using lengthy algorithms. In this talk I will review the theory behind GPC's. I will review its Bayesian formulation, and its parameter estimation methods. In addition, I will present a new algorithm that leads to an exact evaluation of the classification.


Dr. Amir Atiya received his B.S. degree from Cairo University, and M.S. and Ph.D. degrees from Caltech, Pasadena, CA, all in electrical engineering. He is a Professor at the Department of Computer Engineering, Cairo University, currently on temporary leave at Veros Systems in Texas. His research interests are in the areas of machine learning, data mining, Monte Carlo methods, with particular application emphasis on financial markets, and business. He got several awards, such as the Kuwait Prize in 2005.

Last updated: January 20, 2012