Improved Classification for a Data Fusing Kohonen Self Organizing Map Using A Dynamic Thresholding Technique
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 828-831.
Abstract
The use of linear data fusion is a fast developing area in the field of military information and combat systems. However, the use of data fusion in conventional application areas is not as wide spread. To date linear data fusion has been used only in applications in which substantial knowledge of both the problem domain and the sensor devices in use are available. However, in applications such as condition monitoring the problem domain can be very complex, with little or no knowledge about the interactions between measured parameters. This paper describes the use of non-linear self-learning or self-organising systems as a tool for data fusion, since these systems can learn complex interrelationships between a number of parameters, and use this information as a tool for improved classification.
@inproceedings{ijcai1999p118,
title = {Improved Classification for a Data Fusing Kohonen Self Organizing Map Using A Dynamic Thresholding Technique},
author = {Taylor, Odin and MacIntyre, John and Tait, John},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {828--831},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/118},
}
TY - CPAPER TI - Improved Classification for a Data Fusing Kohonen Self Organizing Map Using A Dynamic Thresholding Technique AU - Taylor, Odin AU - MacIntyre, John AU - Tait, John T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 828 EP - 831 UR - https://ijcai.org/proceedings/1999/118 ER -Copied
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