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ICML
2002
IEEE
14 years 9 months ago
Active + Semi-supervised Learning = Robust Multi-View Learning
In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view a...
Ion Muslea, Steven Minton, Craig A. Knoblock
GECCO
2005
Springer
124views Optimization» more  GECCO 2005»
14 years 2 months ago
XCS for robust automatic target recognition
A primary strength of the XCS approach is its ability to create maximally accurate general rules. In automatic target recognition (ATR) there is a need for robust performance beyo...
B. Ravichandran, Avinash Gandhe, Robert E. Smith
TASLP
2002
124views more  TASLP 2002»
13 years 8 months ago
A robust compensation strategy for extraneous acoustic variations in spontaneous speech recognition
In this paper, we propose a robust compensation strategy to deal effectively with extraneous acoustic variations for spontaneous speech recognition. This strategy extends speaker a...
Hui Jiang, Li Deng
CORR
2008
Springer
105views Education» more  CORR 2008»
13 years 9 months ago
Robust Machine Learning Applied to Terascale Astronomical Datasets
We present recent results from the LCDM3 collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the mining of ...
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers
COMPUTING
2004
125views more  COMPUTING 2004»
13 years 8 months ago
Robust Spherical Parameterization of Triangular Meshes
Parameterization of 3D mesh data is important for many graphics and mesh processing applications, in particular for texture mapping, remeshing and morphing. Closed, manifold, genu...
Alla Sheffer, Craig Gotsman, Nira Dyn