TOWARDS LEARNING WITH OBJECTS IN A HIERARCHICAL REPRESENTATION

Nicolas Cebron

Abstract

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.

References

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Paper Citation


in Harvard Style

Cebron N. (2010). TOWARDS LEARNING WITH OBJECTS IN A HIERARCHICAL REPRESENTATION . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 326-329. DOI: 10.5220/0003114403260329


in Bibtex Style

@conference{kdir10,
author={Nicolas Cebron},
title={TOWARDS LEARNING WITH OBJECTS IN A HIERARCHICAL REPRESENTATION},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={326-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003114403260329},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - TOWARDS LEARNING WITH OBJECTS IN A HIERARCHICAL REPRESENTATION
SN - 978-989-8425-28-7
AU - Cebron N.
PY - 2010
SP - 326
EP - 329
DO - 10.5220/0003114403260329