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ICML
2006
IEEE
14 years 11 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
ICML
2004
IEEE
14 years 11 months ago
Multiple kernel learning, conic duality, and the SMO algorithm
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...
CVPR
2007
IEEE
14 years 4 months ago
Local Ensemble Kernel Learning for Object Category Recognition
This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass featu...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
ICRA
2006
IEEE
131views Robotics» more  ICRA 2006»
14 years 4 months ago
Using Reinforcement Learning to Improve Exploration Trajectories for Error Minimization
Abstract— The mapping and localization problems have received considerable attention in robotics recently. The exploration problem that drives mapping has started to generate sim...
Thomas Kollar, Nicholas Roy
CICLING
2005
Springer
14 years 3 months ago
A Machine Learning Approach to Information Extraction
Information extraction is concerned with applying natural language processing to automatically extract the essential details from text documents. A great disadvantage of current ap...
Alberto Téllez-Valero, Manuel Montes-y-G&oa...