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ICML
2010
IEEE
14 years 10 months ago
Learning the Linear Dynamical System with ASOS
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...
James Martens
82
Voted
RECOMB
2007
Springer
15 years 10 months ago
Reconstructing the Topology of Protein Complexes
Abstract. Recent advances in high-throughput experimental techniques have enabled the production of a wealth of protein interaction data, rich in both quantity and variety. While t...
Allister Bernard, David S. Vaughn, Alexander J. Ha...
ICDAR
2005
IEEE
15 years 3 months ago
A Statistical Learning Approach To Document Image Analysis
In the field of computer analysis of document images, the problems of physical and logical layout analysis have been approached through a variety of heuristic, rule-based, and gr...
Kevin Laven, Scott Leishman, Sam T. Roweis
78
Voted
ICML
2008
IEEE
15 years 10 months ago
Empirical Bernstein stopping
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
Csaba Szepesvári, Jean-Yves Audibert, Volod...
RSS
2007
129views Robotics» more  RSS 2007»
14 years 11 months ago
Spatially-Adaptive Learning Rates for Online Incremental SLAM
— Several recent algorithms have formulated the SLAM problem in terms of non-linear pose graph optimization. These algorithms are attractive because they offer lower computationa...
Edwin Olson, John J. Leonard, Seth J. Teller