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.

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Author:Nicolas Cebron
Frontdoor URL
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2011-03)
Publishing Institution:Universität Augsburg
Release Date:2011/03/04
Tag:supervised learning; active learning; object hierarchy
GND-Keyword:Teilüberwachtes Lernen
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht