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» Feature selection in scientific applications
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ICML
2010
IEEE
13 years 11 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
CEC
2010
IEEE
13 years 11 months ago
An analysis of clustering objectives for feature selection applied to encrypted traffic identification
This work explores the use of clustering objectives in a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, under the application...
Carlos Bacquet, A. Nur Zincir-Heywood, Malcolm I. ...
TVCG
2012
180views Hardware» more  TVCG 2012»
12 years 8 days ago
Feature-Driven Data Exploration for Volumetric Rendering
Abstract—We have developed an intuitive method to semi-automatically explore volumetric data in a focus-region-guided or valuedriven way using a user defined ray through the 3D ...
Insoo Woo, Ross Maciejewski, Kelly P. Gaither, Dav...
KDD
2010
ACM
326views Data Mining» more  KDD 2010»
13 years 7 months ago
Document clustering via dirichlet process mixture model with feature selection
One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we ...
Guan Yu, Ruizhang Huang, Zhaojun Wang
KDD
2003
ACM
195views Data Mining» more  KDD 2003»
14 years 10 months ago
Visualizing changes in the structure of data for exploratory feature selection
Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power ava...
Elias Pampalk, Werner Goebl, Gerhard Widmer