Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Pascal Held for a login account.
| This site is powered by Aigaion - A PHP/Web based management system for shared and annotated bibliographies. For more information visit www.aigaion.nl. |
|
Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear {SVM}
| Type of publication: | Inproceedings |
| Citation: | moewes_tackling_2008 |
| Booktitle: | Soft Methods for Handling Variability and Imprecision |
| Series: | Advances in Soft Computing |
| Volume: | 48 |
| Year: | 2008 |
| Month: | October |
| Pages: | 409--416 |
| Publisher: | Springer Berlin/Heidelberg |
| ISSN: | 1615-3871 |
| ISBN: | 978-3-540-85026-7 |
| URL: | http://springerlink.com/conten... |
| DOI: | 10.1007/978-3-540-85027-4_49 |
| Abstract: | In this paper we introduce a preprocessing method for safety-related applications. Since we concentrate on scenarios with highly unbalanced misclassification costs, we briefly discuss a variation of multiple-instance learning (MIL) and recall soft margin hyperplane classifiers; in particular the principle of a support vector machine (SVM). According to this classifier, we present a training set selection method for learning quasilinear SVMs which guarantee both high accuracy and interpretability to a higher degree. We conclude with annotating on a real-world application and potential extensions for future research in this domain. |
| Keywords: | Multiple-Instance Learning, Safety-Related Systems, Support Vector Machine |
| Authors | |
| Editors | |
| Added by: | [ADM] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
|
Topics
|
|
