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» Discovering Classification from Data of Multiple Sources
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ICCV
2009
IEEE
13 years 5 months ago
Incremental Multiple Kernel Learning for object recognition
A good training dataset, representative of the test images expected in a given application, is critical for ensuring good performance of a visual categorization system. Obtaining ...
Aniruddha Kembhavi, Behjat Siddiquie, Roland Miezi...
ICML
2005
IEEE
14 years 8 months ago
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Mark Girolami, Simon Rogers
CORR
2007
Springer
137views Education» more  CORR 2007»
13 years 7 months ago
Cross-Matching Multiple Spatial Observations and Dealing with Missing Data
: Cross-match spatially clusters and organizes several astronomical point-source measurements from one or more surveys. Ideally, each object would be found in each survey. Unfortun...
Jim Gray, Alexander S. Szalay, Tamas Budavari, Rob...
KDD
2005
ACM
91views Data Mining» more  KDD 2005»
14 years 8 months ago
On mining cross-graph quasi-cliques
Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-marke...
Jian Pei, Daxin Jiang, Aidong Zhang
BMCBI
2010
208views more  BMCBI 2010»
13 years 7 months ago
A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are common...
Pengyi Yang, Bing Bing Zhou, Zili Zhang, Albert Y....