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CAEPIA
2003
Springer
14 years 29 days ago
A Method to Adaptively Propagate the Set of Samples Used by Particle Filters
Abstract. In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The ef...
Alvaro Soto
UAI
2008
13 years 9 months ago
Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
John Duchi, Stephen Gould, Daphne Koller
CVPR
2009
IEEE
15 years 2 months ago
Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields
We propose a novel approach for improving level set seg- mentation methods by embedding the potential functions from a discriminatively trained conditional random field (CRF) in...
Dana Cobzas (University of Alberta), Mark Schmidt ...
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
14 years 2 months ago
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon...
SIGIR
2005
ACM
14 years 1 months ago
Robustness of adaptive filtering methods in a cross-benchmark evaluation
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection...
Yiming Yang, Shinjae Yoo, Jian Zhang, Bryan Kisiel