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» Applying Support Vector Machines to Imbalanced Datasets
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KDD
2008
ACM
178views Data Mining» more  KDD 2008»
14 years 8 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
KDD
2006
ACM
201views Data Mining» more  KDD 2006»
14 years 8 months ago
Clustering based large margin classification: a scalable approach using SOCP formulation
This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture...
J. Saketha Nath, Chiranjib Bhattacharyya, M. Naras...
CVPR
2008
IEEE
14 years 9 months ago
Margin-based discriminant dimensionality reduction for visual recognition
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
Hakan Cevikalp, Bill Triggs, Frédéri...
COLT
2001
Springer
14 years 7 days ago
Limitations of Learning via Embeddings in Euclidean Half-Spaces
The notion of embedding a class of dichotomies in a class of linear half spaces is central to the support vector machines paradigm. We examine the question of determining the mini...
Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon
BMCBI
2005
125views more  BMCBI 2005»
13 years 7 months ago
A simple approach for protein name identification: prospects and limits
Background: Significant parts of biological knowledge are available only as unstructured text in articles of biomedical journals. By automatically identifying gene and gene produc...
Katrin Fundel, Daniel Güttler, Ralf Zimmer, J...