### Abstract

Original language | Unknown |
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Title of host publication | Information Fusion and Geographic Information Systems, Proceedings |

Editors | VV Popovich, M Schrenk, C Claramunt, KV Korolenko |

Place of Publication | Berlin |

Publisher | Springer-Verlag |

Pages | 19-36 |

ISBN (Print) | 1863-2246 978-3-642-00303-5 |

Publication status | Published - 1 Jan 2009 |

### Publication series

Name | Lecture Notes in Geoinformation and Cartography |
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Publisher | Springer-Verlag |

### Cite this

*Information Fusion and Geographic Information Systems, Proceedings*(pp. 19-36). (Lecture Notes in Geoinformation and Cartography). Berlin: Springer-Verlag.

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*Information Fusion and Geographic Information Systems, Proceedings.*Lecture Notes in Geoinformation and Cartography, Springer-Verlag, Berlin, pp. 19-36.

**Application of Self-Organizing Maps to the Maritime Environment.** / Lobo, Vitor José Almeida Sousa.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Application of Self-Organizing Maps to the Maritime Environment

AU - Lobo, Vitor José Almeida Sousa

N1 - ISI Document Delivery No.: BMU62 Times Cited: 0 Cited Reference Count: 77 Lobo, Victor J. A. S. Proceedings Paper 4th International Workshop on Information Fusion and Geographical Information Systems May 17-20, 2009 St Petersburg, RUSSIA RAS, St Petersburg Inst Informat & Automat Heidelberger platz 3, d-14197 berlin, germany

PY - 2009/1/1

Y1 - 2009/1/1

N2 - Self-Organizing Maps (SOMs), or Kohonen networks, are widely used neural network architecture. This paper starts with a brief overview of how SOMs can be used in different types of problems. A simple and intuitive explanation of how a SOM is trained is provided, together with a formal explanation of the algorithm, and some of the more important parameters are discussed. Finally, an overview of different applications of SOMs in maritime problems is presented.

AB - Self-Organizing Maps (SOMs), or Kohonen networks, are widely used neural network architecture. This paper starts with a brief overview of how SOMs can be used in different types of problems. A simple and intuitive explanation of how a SOM is trained is provided, together with a formal explanation of the algorithm, and some of the more important parameters are discussed. Finally, an overview of different applications of SOMs in maritime problems is presented.

KW - algorithms

KW - spectra

KW - Self-organizing

KW - maps

KW - florida

KW - Kohonen

KW - pattern-recognition

KW - neural-networks

KW - networks

KW - classification

KW - variability

KW - tracking

KW - sea

KW - shelf

KW - SOM

KW - som

KW - west

M3 - Chapter

SN - 1863-2246 978-3-642-00303-5

T3 - Lecture Notes in Geoinformation and Cartography

SP - 19

EP - 36

BT - Information Fusion and Geographic Information Systems, Proceedings

A2 - Popovich, VV

A2 - Schrenk, M

A2 - Claramunt, C

A2 - Korolenko, KV

PB - Springer-Verlag

CY - Berlin

ER -