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» Optimizing Data Transformations for Classification Tasks
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ICML
2006
IEEE
14 years 8 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
DASFAA
2009
IEEE
195views Database» more  DASFAA 2009»
14 years 2 months ago
The XMLBench Project: Comparison of Fast, Multi-platform XML libraries
The XML technologies have brought a lot of new ideas and abilities in the field of information management systems. Nowadays, XML is used almost everywhere: from small configurati...
Suren Chilingaryan
PAMI
2011
13 years 2 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
PAMI
2000
142views more  PAMI 2000»
13 years 7 months ago
Evolutionary Pursuit and Its Application to Face Recognition
Abstract-- This paper introduces Evolutionary Pursuit (EP) as a novel and adaptive representation method for image encoding and classification. In analogy to projection pursuit met...
Chengjun Liu, Harry Wechsler
ECCV
2004
Springer
14 years 9 months ago
Dimensionality Reduction by Canonical Contextual Correlation Projections
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
Marco Loog, Bram van Ginneken, Robert P. W. Duin