Authors:
Dragos Calitoiu
1
and
B. John Oommen
2
Affiliations:
1
Carleton University, Canada
;
2
Carleton University;University of Agder, Norway
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
theory 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.
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