A knowledge-intensive, integrated approach to problem solving and sustained learning
A knowledge-intensive, integrated approach to problem solving and sustained learning
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
FSfRT: Forecasting System for Red Tides
Applied Intelligence
Explanation in Case-Based Reasoning---Perspectives and Goals
Artificial Intelligence Review
ViSOM ensembles for visualization and classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEEE Transactions on Neural Networks
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The hybrid intelligent system presented here, forecasts the presence or not of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. The proposed CBR includes a novel network for data classification and data retrieval. Such network works as a summarization algorithm for the results of an ensemble of Visualization Induced Self-Organizing Maps. This algorithm, called Weighted Voting Superposition (WeVoS), is mainly aimed to achieve the lowest topographic error in the map. The system uses information obtained from various satellites such as salinity, temperature, pressure, number and area of the slicks. WeVoS-CBR system has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.