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» Supervised Feature Extraction Using Hilbert-Schmidt Norms
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ER
2003
Springer
98views Database» more  ER 2003»
15 years 4 months ago
Extracting Relations from XML Documents
XML is becoming a prevalent format for data exchange. Many XML documents have complex schemas that are not always known, and can vary widely between information sources and applica...
Eugene Agichtein, C. T. Howard Ho, Vanja Josifovsk...
HAIS
2009
Springer
15 years 4 months ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...
KDD
2000
ACM
133views Data Mining» more  KDD 2000»
15 years 3 months ago
Data selection for support vector machine classifiers
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
Glenn Fung, Olvi L. Mangasarian
AIRS
2006
Springer
15 years 3 months ago
A Semantic Fusion Approach Between Medical Images and Reports Using UMLS
One of the main challenges in content-based image retrieval still remains to bridge the gap between low-level features and semantic information. In this paper, we present our first...
Daniel Racoceanu, Caroline Lacoste, Roxana Teodore...
LREC
2008
150views Education» more  LREC 2008»
15 years 1 months ago
Causal Relation Extraction
This paper presents a supervised method for the detection and extraction of Causal Relations from open domain text. First we give a brief outline of the definition of causation an...
Eduardo Blanco, Núria Castell, Dan I. Moldo...