Towards learning with objects in a hierarchical representation
- In most supervised learning tasks, objects are perceived as a collection of fixed attribute values. In this work, we try to extend this notion to a hierarchy of attribute sets with different levels of quality. When we are given the objects in this representation, we might consider to learn from most examples at the lowest quality level and only to enhance a few examples for the classification algorithm. We propose an approach for selecting those interesting objects and demonstrate its superior performance to random selection.
| Author: | Nicolas Cebron |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-612741 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/61274 |
| ISBN: | 978-989-8425-28-7OPAC |
| Parent Title (English): | Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - KDIR, October 25-28, 2010, in Valencia, Spain |
| Publisher: | SciTePress |
| Place of publication: | Setúbal |
| Editor: | Ana Fred, Joaquim Filipe |
| Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2019/08/30 |
| Year of first Publication: | 2010 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2019/08/30 |
| Issue: | Volume 1 |
| First Page: | 326 |
| Last Page: | 329 |
| DOI: | https://doi.org/10.5220/0003114403260329 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung |
