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» Learning the Relative Importance of Features in Image Data
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ICML
2006
IEEE
14 years 9 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
CVPR
2007
IEEE
14 years 10 months ago
Image-Based Localization Using Hybrid Feature Correspondences
Where am I and what am I seeing? This is a classical vision problem and this paper presents a solution based on efficient use of a combination of 2D and 3D features. Given a model...
Fredrik Kahl, Kalle Åström, Klas Joseph...
ICRA
2010
IEEE
108views Robotics» more  ICRA 2010»
13 years 7 months ago
Robust place recognition for 3D range data based on point features
Abstract— The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Wherea...
Bastian Steder, Giorgio Grisetti, Wolfram Burgard
ICASSP
2011
IEEE
13 years 10 days ago
Generic object recognition using automatic region extraction and dimensional feature integration utilizing multiple kernel learn
Recently, in generic object recognition research, a classification technique based on integration of image features is garnering much attention. However, with a classifying techn...
Toru Nakashika, Akira Suga, Tetsuya Takiguchi, Yas...
PKDD
2009
Springer
148views Data Mining» more  PKDD 2009»
14 years 3 months ago
Feature Selection by Transfer Learning with Linear Regularized Models
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...
Thibault Helleputte, Pierre Dupont