Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15822-3_11 | ICANN (2) |
Keywords | Field | DocType |
novel approach,relevant feature,inherent feature selection capability,zero norm,single training run,feature machine,support feature machine,so-called support,toy example,feature selection | k-nearest neighbors algorithm,Dimensionality reduction,Pattern recognition,Feature selection,Computer science,Feature (computer vision),Feature model,Feature (machine learning),Artificial intelligence,Linear classifier,Classifier (linguistics),Machine learning | Conference |
Volume | ISSN | ISBN |
6353 | 0302-9743 | 3-642-15821-8 |
Citations | PageRank | References |
5 | 0.54 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sascha Klement | 1 | 24 | 3.26 |
| Thomas Martinetz | 2 | 1462 | 231.48 |