A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION

Klaus Häming, Gabriele Peters

Abstract

We propose a hybrid learning system which combines two different theories of learning, namely implicit and explicit learning. They are realized by the machine learning methods of reinforcement learning and belief revision, respectively. The resulting system can be regarded as an autonomous agent which is able to learn from past experiences as well as to acquire new knowledge from its environment. We apply this agent in an object recognition task, where it learns how to recognize a 3D object despite the fact that a very similar, alternative object exists. The agent scans the viewing sphere of an object and learns how to access such a view that allows for the discrimination. We present first experiments which indicate the general applicability of the proposed hybrid learning scheme to this object recognition tasks.

References

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Paper Citation


in Harvard Style

Häming K. and Peters G. (2011). A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8425-75-1, pages 329-332. DOI: 10.5220/0003572103290332


in Bibtex Style

@conference{icinco11,
author={Klaus Häming and Gabriele Peters},
title={A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2011},
pages={329-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003572103290332},
isbn={978-989-8425-75-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION
SN - 978-989-8425-75-1
AU - Häming K.
AU - Peters G.
PY - 2011
SP - 329
EP - 332
DO - 10.5220/0003572103290332