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.

Download full text files

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Nicolas Cebron
URN:urn:nbn:de:bvb:384-opus4-612741
Frontdoor URLhttps://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
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 (mit Print on Demand)