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» New Algorithms for Learning in Presence of Errors
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FSTTCS
2006
Springer
14 years 11 days ago
Normal and Feature Approximations from Noisy Point Clouds
We consider the problem of approximating normal and feature sizes of a surface from point cloud data that may be noisy. These problems are central to many applications dealing wit...
Tamal K. Dey, Jian Sun
SIAMJO
2002
122views more  SIAMJO 2002»
13 years 8 months ago
Robust Filtering via Semidefinite Programming with Applications to Target Tracking
In this paper we propose a novel finite-horizon, discrete-time, time-varying filtering method based on the robust semidefinite programming (SDP) technique. The proposed method prov...
Lingjie Li, Zhi-Quan Luo, Timothy N. Davidson, Kon...
ICPR
2010
IEEE
13 years 9 months ago
Incorporating Linguistic Model Adaptation into Whole-Book Recognition
Abstract—Whole-book recognition is a document image analysis strategy that operates on the complete set of a book’s page images using automatic adaptation to improve accuracy. ...
Pingping Xiu, Henry S. Baird
ECML
2007
Springer
14 years 2 months ago
Additive Groves of Regression Trees
We present a new regression algorithm called Additive Groves and show empirically that it is superior in performance to a number of other established regression methods. A single G...
Daria Sorokina, Rich Caruana, Mirek Riedewald
ECML
1998
Springer
14 years 28 days ago
A Monotonic Measure for Optimal Feature Selection
Feature selection is a problem of choosing a subset of relevant features. Researchers have been searching for optimal feature selection methods. `Branch and Bound' and Focus a...
Huan Liu, Hiroshi Motoda, Manoranjan Dash