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ESANN
2008
14 years 11 months ago
Interpretable ensembles of local models for safety-related applications
Abstract. This paper discusses a machine learning approach for binary classification problems which satisfies the specific requirements of safety-related applications. The approach...
Sebastian Nusser, Clemens Otte, Werner Hauptmann
IJCAI
1997
14 years 11 months ago
Is Nonparametric Learning Practical in Very High Dimensional Spaces?
Many of the challenges faced by the £eld of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and ...
Gregory Z. Grudic, Peter D. Lawrence
ICCV
2005
IEEE
15 years 11 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
IPMI
2003
Springer
15 years 10 months ago
Learning Object Correspondences with the Observed Transport Shape Measure
Abstract. We propose a learning method which introduces explicit knowledge to the object correspondence problem. Our approach uses an a priori learning set to compute a dense corre...
Alain Pitiot, Hervé Delingette, Arthur W. T...
129
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CVPR
2010
IEEE
15 years 6 months ago
Learning Full Pairwise Affinities for Spectral Segmentation
This paper studies the problem of learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. The overall quality of the spect...
Tae Hoon Kim (Seoul National University), Kyoung M...