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Authors: Nico Piatkowski ; Sangkyun Lee and Katharina Morik

Affiliation: TU Dortmund University, Germany

Keyword(s): Graphical Models, Approximate Inference.

Related Ontology Subjects/Areas/Topics: Exact and Approximate Inference ; Graphical and Graph-Based Models ; Pattern Recognition ; Theory and Methods

Abstract: Machine learning on resource constrained ubiquitous devices suffers from high energy consumption and slow execution time. In this paper, it is investigated how to modify machine learning algorithms in order to reduce the number of consumed clock cycles—not by reducing the asymptotic complexity, but by assuming a weaker execution platform. In particular, an integer approximation to the class of undirected graphical models is proposed. Algorithms for inference, maximum-a-posteriori prediction and parameter estimation are presented and approximation error is discussed. In numerical evaluations on synthetic data, the response of the model to several influential properties of the data is investigated. The results on the synthetic data are confirmed with a natural language processing task on an open data set. In addition, the runtime on low-end hardware is regarded. The overall speedup of the new algorithms is at least 2× while overall loss in accuracy is rather small. This allows running probabilistic methods on very small devices, even if they do not contain a processor that is capable of executing floating point arithmetic at all. (More)

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Paper citation in several formats:
Piatkowski, N.; Lee, S. and Morik, K. (2014). The Integer Approximation of Undirected Graphical Models. In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-018-5; ISSN 2184-4313, SciTePress, pages 296-304. DOI: 10.5220/0004831202960304

@conference{icpram14,
author={Nico Piatkowski. and Sangkyun Lee. and Katharina Morik.},
title={The Integer Approximation of Undirected Graphical Models},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2014},
pages={296-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004831202960304},
isbn={978-989-758-018-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - The Integer Approximation of Undirected Graphical Models
SN - 978-989-758-018-5
IS - 2184-4313
AU - Piatkowski, N.
AU - Lee, S.
AU - Morik, K.
PY - 2014
SP - 296
EP - 304
DO - 10.5220/0004831202960304
PB - SciTePress