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» Structure feature selection for graph classification
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ICML
2004
IEEE
15 years 10 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
ICMLA
2009
14 years 7 months ago
Text Classification Methodologies Applied to Micro-Text in Military Chat
We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologi...
Kevin Dela Rosa, Jeffrey Ellen
CVPR
2005
IEEE
15 years 11 months ago
Diagram Structure Recognition by Bayesian Conditional Random Fields
Hand-drawn diagrams present a complex recognition problem. Elements of the diagram are often individually ambiguous, and require context to be interpreted. We present a recognitio...
Yuan (Alan) Qi, Martin Szummer, Thomas P. Minka
APBC
2003
128views Bioinformatics» more  APBC 2003»
14 years 11 months ago
Machine Learning in DNA Microarray Analysis for Cancer Classification
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it e...
Sung-Bae Cho, Hong-Hee Won
SIGDIAL
2010
14 years 7 months ago
Discourse indicators for content selection in summarization
We present analyses aimed at eliciting which specific aspects of discourse provide the strongest indication for text importance. In the context of content selection for single doc...
Annie Louis, Aravind K. Joshi, Ani Nenkova