Corpus ID: 85529030

SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python

  title={SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python},
  author={Felix M. Riese and Sina Keller},
In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need for machine learning techniques which are well-suited for these different datasets. One possible technique is the self-organizing map (SOM), a type of artificial neural network which is, so far, weakly represented in the field of machine learning. The SOM’s unique characteristic is the neighborhood relationship of the output neurons. This relationship improves the ability of generalization on small… Expand
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  • T. Kohonen
  • Computer Science, Medicine
  • Neural Networks
  • 2013
The self-organizing map (SOM) is an automatic data-analysis method widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics and can be found in the management of massive textual databases and in bioinformatics. Expand
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Recent changes in package kohonen are described, implementing several different forms of SOMs, primarily focused on making the package more useable for large data sets. Expand
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The two-stage procedure--first using SOM to produce the prototypes that are then clustered in the second stage--is found to perform well when compared with direct clustering of the data and to reduce the computation time. Expand
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Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application
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  • IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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