Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

19

ICANN
2010
Springer

favoriteEmaildiscussreport

152views Neural Networks» more ICANN 2010»

The Support Feature Machine for Classifying with the Least Number of Features

13 years 2 months ago

The Support Feature Machine for Classifying with the Least Number of Features

Download www.inb.uni-luebeck.de

We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively. Key words: Support feature machine, feature selection, zero norm minimisation, classification.

Sascha Klement, Thomas Martinetz

Real-time Traffic

ICANN 2010 | Inherent Feature Selection | Neural Networks | Separating Hyperplane | Zero Norm |

claim paper

Related Content

» A New Approach to Classification with the Least Number of Features

» Regularized Least Squares Cancer Classifiers from DNA microarray data

» How good are support vector machines

» Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimizat...

» Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm

» Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation...

» Multiclass feature selection for texture classification

» Object Detection in Images RunTime Complexity and Parameter Selection of Support Vector Ma...

» FSSFS A novel feature selection method for support vector machines

Post Info
More Details (n/a)

Added	11 Feb 2011
Updated	11 Feb 2011
Type	Journal
Year	2010
Where	ICANN
Authors	Sascha Klement, Thomas Martinetz

Comments (0)