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TNN
2010
216views Management» more  TNN 2010»
14 years 4 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok
ICPR
2000
IEEE
15 years 10 months ago
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
Mário A. T. Figueiredo
AAAI
2008
15 years 1 days ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...

Publication
700views
16 years 6 months ago
Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems - [ Mumford-Shah ]
"The purpose of this paper is to introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision. In com...
David Mumford and Jayant Shah
SI3D
2006
ACM
15 years 3 months ago
View-dependent precomputed light transport using nonlinear Gaussian function approximations
We propose a real-time method for rendering rigid objects with complex view-dependent effects under distant all-frequency lighting. Existing precomputed light transport approaches...
Paul Green, Jan Kautz, Wojciech Matusik, Fré...