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BIOINFORMATICS
2006
92views more  BIOINFORMATICS 2006»
15 years 2 months ago
What should be expected from feature selection in small-sample settings
Motivation: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; howev...
Chao Sima, Edward R. Dougherty
125
Voted
PR
2006
229views more  PR 2006»
15 years 2 months ago
FS_SFS: A novel feature selection method for support vector machines
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of "overfitting". Feature Selection addre...
Yi Liu, Yuan F. Zheng
ICML
2003
IEEE
16 years 3 months ago
Incorporating Diversity in Active Learning with Support Vector Machines
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
Klaus Brinker
135
Voted
NAACL
2004
15 years 4 months ago
Shallow Semantic Parsing using Support Vector Machines
In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algor...
Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James ...
99
Voted
ICDAR
2007
IEEE
15 years 9 months ago
Hybrid Mathematical Symbol Recognition Using Support Vector Machines
Recognition of mathematical symbols is a challenging task, with a large set with many similar symbols. We present a support vector machine based hybrid recognition system that use...
Birendra Keshari, Stephen M. Watt