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Authors: Hendrik Annuth and Christian-A. Bohn

Affiliation: Wedel University of Applied Sciences, Germany

ISBN: 978-989-8425-32-4

Keyword(s): Neural networks, Unsupervised learning, Self-organization, Growing cells structures, Surface reconstruction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: General unsupervised learning or self-organization places n-dimensional reference vectors in order to match the distribution of samples in an n-dimensional vector space. Beside this abstract view on self-organization there are many applications where training — focused on the sample distribution only — does not lead to a satisfactory match between reference cells and samples. Kohonen’s self-organizing map, for example, overcomes pure unsupervised learning by augmenting an additional 2D topology. And although pure unsupervised learning is restricted therewith, the result is valuable in applications where an additional 2D structure hidden in the sample distribution should be recognized. In this work, we generalize this idea of application-focused trimming of ideal, unsupervised learning and reinforce it through the application of surface reconstruction from 3D point samples. Our approach is based on Fritzke’s growing cells structures (GCS) (Fritzke, 1993) which we extend to the smart gr owing cells (SGC) by grafting cells by a higher-level intelligence beyond the classical distribution matching capabilities. Surface reconstruction with smart growing cells outperforms most neural network based approaches and it achieves several advantages compared to classical reconstruction methods. (More)

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Paper citation in several formats:
Annuth H. and Bohn C. (2010). SMART GROWING CELLS.In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 227-237. DOI: 10.5220/0003085202270237

@conference{icnc10,
author={Hendrik Annuth and Christian-A. Bohn},
title={SMART GROWING CELLS},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={227-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003085202270237},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - SMART GROWING CELLS
SN - 978-989-8425-32-4
AU - Annuth H.
AU - Bohn C.
PY - 2010
SP - 227
EP - 237
DO - 10.5220/0003085202270237

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