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IJDMMM
2008
87views more  IJDMMM 2008»
13 years 10 months ago
Is an ordinal class structure useful in classifier learning?
In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, t...
Jens C. Huhn, Eyke Hüllermeier
CORR
2008
Springer
105views Education» more  CORR 2008»
13 years 10 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
ICASSP
2011
IEEE
13 years 1 months ago
Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications
Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting th...
Kyong-Ho Lee, Sun-Yuan Kung, Naveen Verma
GEOINFO
2007
13 years 11 months ago
Comparison of Machine Learning Algorithms for Mapping the Phytophysiognomies of the Brazilian Cerrado
This present work describes the classification of the Phytophysiognomies present in the Brazilian Cerrado biome through the means Artificial Intelligence; data from remote sensing ...
Luciano T. de Oliveira, Thomaz C. de A. Oliveira, ...
TNN
2010
176views Management» more  TNN 2010»
13 years 4 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao