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Authors: Dragos Calitoiu 1 and B. John Oommen 2

Affiliations: 1 Carleton University, Canada ; 2 Carleton University;University of Agder, Norway

ISBN: 978-989-674-021-4

Keyword(s): Disease outbreak, Stochastic point location, Learning automata.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: Pattern Recognition (PR) involves two phases, a Training phase and a Testing Phase. The problems associated with training a classifier when the number of training samples is small are well recorded. Typically, the matrices involved are ill-conditioned and the estimates of the probability distributions are very inaccurate, leading to a very poor classification system. In this paper, we report what we believe are the pioneering results for designing a PR system when there are absolutely no training samples. In such a scenario, we show how we can use a model of the underlying phenomenon and combine it with the principle of stochastic learning to design a very good classifier. By way of example, we consider the case of disease outbreak: Learning the Contagion Parameter in a black box model involving healthy, sick and contagious individuals. The parameter of interest involves Ƞ which is the probability with which an infected person will transmit the disease to a healthy person. Using the t heory of Stochastic Point Location (SPL), the problem is reduced to a PR or classification problem in which the SPL is first subjected to a training phase, the outcome of which is used for the testing phase. (More)

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Paper citation in several formats:
Calitoiu D.; John Oommen B. and (2010). ON USING SIMULATION AND STOCHASTIC LEARNING FOR PATTERN RECOGNITION WHEN TRAINING DATA IS UNAVAILABLE - The Case of Disease Outbreak.In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 45-52. DOI: 10.5220/0002716800450052

@conference{icaart10,
author={Dragos Calitoiu and B. {John Oommen}},
title={ON USING SIMULATION AND STOCHASTIC LEARNING FOR PATTERN RECOGNITION WHEN TRAINING DATA IS UNAVAILABLE - The Case of Disease Outbreak},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={45-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002716800450052},
isbn={978-989-674-021-4},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - ON USING SIMULATION AND STOCHASTIC LEARNING FOR PATTERN RECOGNITION WHEN TRAINING DATA IS UNAVAILABLE - The Case of Disease Outbreak
SN - 978-989-674-021-4
AU - Calitoiu, D.
AU - John Oommen, B.
PY - 2010
SP - 45
EP - 52
DO - 10.5220/0002716800450052

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