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» Methods for Dynamic Classifier Selection
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ECCV
2010
Springer
13 years 8 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
IJCAI
2007
13 years 9 months ago
Document Summarization Using Conditional Random Fields
Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task ...
Dou Shen, Jian-Tao Sun, Hua Li, Qiang Yang, Zheng ...
BMCBI
2006
143views more  BMCBI 2006»
13 years 7 months ago
Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms
Background: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information nee...
Rainer König, Gunnar Schramm, Marcus Oswald, ...
SADM
2010
141views more  SADM 2010»
13 years 2 months ago
A parametric mixture model for clustering multivariate binary data
: The traditional latent class analysis (LCA) uses a mixture model with binary responses on each subject that are independent conditional on cluster membership. However, in many pr...
Ajit C. Tamhane, Dingxi Qiu, Bruce E. Ankenman
JORS
2010
189views more  JORS 2010»
13 years 2 months ago
Monte Carlo scenario generation for retail loan portfolios
Monte Carlo simulation is a common method for studying the volatility of market traded instruments. It is less employed in retail lending, because of the inherent nonlinearities in...
J. L. Breeden, D. Ingram