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AI
2002
Springer
13 years 8 months ago
Ensembling neural networks: Many could be better than all
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its compone...
Zhi-Hua Zhou, Jianxin Wu, Wei Tang
ICDM
2009
IEEE
205views Data Mining» more  ICDM 2009»
14 years 3 months ago
Active Selection of Sensor Sites in Remote Sensing Applications
— In a data-mining approach, a model for estimation of Aerosol Optical Depth (AOD) from satellite observations is learned using collocated satellite and groundbased observations....
Debasish Das, Zoran Obradovic, Slobodan Vucetic
CVPR
2010
IEEE
14 years 4 months ago
Efficiently Selecting Regions for Scene Understanding
Recent advances in scene understanding and related tasks have highlighted the importance of using regions to reason about high-level scene structure. Typically, the regions are ...
M. Pawan Kumar, Daphne Koller
WWW
2009
ACM
14 years 9 months ago
An axiomatic approach for result diversification
Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diver...
Sreenivas Gollapudi, Aneesh Sharma
SPLC
2008
13 years 10 months ago
Filtered Cartesian Flattening: An Approximation Technique for Optimally Selecting Features while Adhering to Resource Constraint
Software Product-lines (SPLs) use modular software components that can be reconfigured into different variants for different requirements sets. Feature modeling is a common method...
Jules White, B. Doughtery, Douglas C. Schmidt