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» Maximal Vector Computation in Large Data Sets
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SDM
2009
SIAM
175views Data Mining» more  SDM 2009»
14 years 6 months ago
Low-Entropy Set Selection.
Most pattern discovery algorithms easily generate very large numbers of patterns, making the results impossible to understand and hard to use. Recently, the problem of instead sel...
Hannes Heikinheimo, Jilles Vreeken, Arno Siebes, H...
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
14 years 3 months ago
Sparse Kernel SVMs via Cutting-Plane Training
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. ...
Thorsten Joachims, Chun-Nam John Yu
CVPR
2008
IEEE
14 years 11 months ago
Object categorization using co-occurrence, location and appearance
In this work we introduce a novel approach to object categorization that incorporates two types of context ? cooccurrence and relative location ? with local appearancebased featur...
Carolina Galleguillos, Andrew Rabinovich, Serge Be...
ICPR
2000
IEEE
14 years 10 months ago
Comparison of Face Verification Results on the XM2VTS Database
The paper presents results of the face verification contest that was organized in conjunction with International Conference on Pattern Recognition 2000 [14]. Participants had to u...
Jiri Matas, Miroslav Hamouz, Kenneth Jonsson, Jose...
ICDAR
2005
IEEE
14 years 2 months ago
Document Ranking by Layout Relevance
This paper describes the development of a new document ranking system based on layout similarity. The user has a need represented by a set of ”wanted” documents, and the syste...
May Huang, Daniel DeMenthon, David S. Doermann, Ly...