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Amir massoud FarahmandPh.D. StudentDepartment of Computing Science University of Alberta 2-21 Athabasca Hall Edmonton, Alberta Canada T6G 2E8 email: ![]() |
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Our NIPS paper on Regularized Policy Iteration has been accepted. I'll provide more information soon! (Oct 2008)
Towards Manifold-Adaptive Learning has been presented in the Topology Learning workshop at NIPS. The main message is that many conventional machine learning methods may actually be manifold-adaptive, i.e. if seemingly high-dimensional data actually come from a low-dimensional manifold, such a manifold-adaptive method behaves as if it is dealing with a low-dimensional problem. We presented two manifold-adaptivity results: one for dimension estimation and another for the traditional K-NN regressor. (Dec 2007)
Research Interests
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