Abstract
This paper presents the application of special unsupervised neural networks (Self-Organizing Maps) to different domains, such as sleep apnea discovery, protein sequence analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical levels enables the discovery of complex stru ctures as present in this type of applications. Finally we recommend the use of regression-type models for Kohonen’s Self-Organizing Networkin gene expression data analysis.
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Guimarães, G., Urfer, W. (2003). Self-Organizing Maps and its Applications in Sleep Apnea Research and Molecular Genetics. In: Schwaiger, M., Opitz, O. (eds) Exploratory Data Analysis in Empirical Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55721-7_34
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DOI: https://doi.org/10.1007/978-3-642-55721-7_34
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