Self-Organizing Maps and its Applications in Sleep Apnea Research and Molecular Genetics
Gabriela Guimarães, Wolfgang Urfer · RePEc: Research Papers in Economics · 2000
This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical levels enables the discovery of complex structures as present in this type of applications. Furthermore, an integration of unsupervised neural networks with hidden markov models is proposed.