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» Learning to learn with the informative vector machine
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COLT
2004
Springer
15 years 10 months ago
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be u...
Peter L. Bartlett, Ambuj Tewari
156
Voted
AMS
2007
Springer
250views Robotics» more  AMS 2007»
15 years 8 months ago
SVMs for Vibration-Based Terrain Classification
When an outdoor mobile robot traverses different types of ground surfaces, different types of vibrations are induced in the body of the robot. These vibrations can be used to learn...
Christian Weiss, Matthias Stark, Andreas Zell
165
Voted
DAGM
2007
Springer
15 years 8 months ago
Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification
Cascades of classifiers constitute an important architecture for fast object detection. While boosting of simple (weak) classifiers provides an established framework, the design of...
Rezaul Karim, Martin Bergtholdt, Jörg H. Kapp...
ACL
2008
15 years 6 months ago
Robust Extraction of Named Entity Including Unfamiliar Word
This paper proposes a novel method to extract named entities including unfamiliar words which do not occur or occur few times in a training corpus using a large unannotated corpus...
Masatoshi Tsuchiya, Shinya Hida, Seiichi Nakagawa
ESANN
2008
15 years 6 months ago
GeoKernels: modeling of spatial data on geomanifolds
This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental ...
Alexei Pozdnoukhov, Mikhail F. Kanevski