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ECCV
2010
Springer
15 years 2 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
BMCBI
2006
166views more  BMCBI 2006»
15 years 1 months ago
SEQOPTICS: a protein sequence clustering system
Background: Protein sequence clustering has been widely used as a part of the analysis of protein structure and function. In most cases single linkage or graph-based clustering al...
Yonghui Chen, Kevin D. Reilly, Alan P. Sprague, Zh...
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
13 years 4 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
GG
2004
Springer
15 years 7 months ago
Workshop on Graph-Based Tools
Graphs are well-known, well-understood, and frequently used means to depict networks of related items. They are successfully used as the underlying mathematical concept in various ...
Tom Mens, Andy Schürr, Gabriele Taentzer
CIKM
2009
Springer
15 years 8 months ago
Reducing the risk of query expansion via robust constrained optimization
We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a r...
Kevyn Collins-Thompson