There are many variants of the original self-organizing neural map algorithm proposed by Kohonen. One of the most recent is the Evolving Tree, a tree-shaped self-organizing network which has many interesting characteristics. This network builds a tree structure splitting the input dataset during learning. This paper presents a speed-up modification of the original training algorithm useful when the Evolving Tree network is used with complex data as images or video. After a measurement of the effectiveness an application of the modified algorithm in image segmentation is presented.

Evolving Tree Algorithm Modifications

Riccardo Rizzo;
2007

Abstract

There are many variants of the original self-organizing neural map algorithm proposed by Kohonen. One of the most recent is the Evolving Tree, a tree-shaped self-organizing network which has many interesting characteristics. This network builds a tree structure splitting the input dataset during learning. This paper presents a speed-up modification of the original training algorithm useful when the Evolving Tree network is used with complex data as images or video. After a measurement of the effectiveness an application of the modified algorithm in image segmentation is presented.
2007
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Inglese
Hujun Yin, Peter Tino, Emilio Corchado, Will Byrne, Xin Yao
Intelligent Data Engineering and Automated Learning - IDEAL 2007
IDEAL 2007
4881
356
364
978-3-540-77225-5
Springer
London
REGNO UNITO DI GRAN BRETAGNA
Sì, ma tipo non specificato
2007
Birmingham, UK
Evolving Tree neural network
3
none
Pirrone, Roberto; Rizzo, Riccardo; Cannella, Vincenzo
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/13024
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