ViSOM ensembles for visualization and classification

Bruno Baraque; Emilio Corchado; Hujun Yin

ViSOM ensembles for visualization and classification

Bruno Baraque, Emilio Corchado, Hujun Yin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper ensemble techniques have been applied in the frame of topology preserving mappings in two applications: classification and visualization. These techniques are applied for the first time to the ViSOM and their performance is compared with ensemble combination of some other topology preserving mapping such as the SOM or the MLSIM. Several methods to obtain a meaningful combination of the components of an ensemble are presented and tested together with the existing ones in order to identify the best performing method in the applications of these models. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Publisher	Springer Nature
Pages	235-243
Number of pages	8
Volume	4507
ISBN (Print)	9783540730064
Publication status	Published - 2007
Event	9th International Work-Conference on Artificial Neural Networks, IWANN 2007 - San Sebastian Duration: 1 Jul 2007 → …

Conference

Conference	9th International Work-Conference on Artificial Neural Networks, IWANN 2007
City	San Sebastian
Period	1/07/07 → …

Cite this

@inproceedings{e28cc8089e1f4a1a9798f7f4c530630d,

title = "ViSOM ensembles for visualization and classification",

abstract = "In this paper ensemble techniques have been applied in the frame of topology preserving mappings in two applications: classification and visualization. These techniques are applied for the first time to the ViSOM and their performance is compared with ensemble combination of some other topology preserving mapping such as the SOM or the MLSIM. Several methods to obtain a meaningful combination of the components of an ensemble are presented and tested together with the existing ones in order to identify the best performing method in the applications of these models. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

author = "Bruno Baraque and Emilio Corchado and Hujun Yin",

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booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.",

publisher = "Springer Nature",

address = "United States",

note = "9th International Work-Conference on Artificial Neural Networks, IWANN 2007 ; Conference date: 01-07-2007",

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Baraque, B, Corchado, E & Yin, H 2007, ViSOM ensembles for visualization and classification. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 4507, Springer Nature, pp. 235-243, 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastian, 1/07/07.

ViSOM ensembles for visualization and classification. / Baraque, Bruno; Corchado, Emilio; Yin, Hujun.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 4507 Springer Nature, 2007. p. 235-243.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ViSOM ensembles for visualization and classification

AU - Baraque, Bruno

AU - Corchado, Emilio

AU - Yin, Hujun

PY - 2007

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N2 - In this paper ensemble techniques have been applied in the frame of topology preserving mappings in two applications: classification and visualization. These techniques are applied for the first time to the ViSOM and their performance is compared with ensemble combination of some other topology preserving mapping such as the SOM or the MLSIM. Several methods to obtain a meaningful combination of the components of an ensemble are presented and tested together with the existing ones in order to identify the best performing method in the applications of these models. © Springer-Verlag Berlin Heidelberg 2007.

AB - In this paper ensemble techniques have been applied in the frame of topology preserving mappings in two applications: classification and visualization. These techniques are applied for the first time to the ViSOM and their performance is compared with ensemble combination of some other topology preserving mapping such as the SOM or the MLSIM. Several methods to obtain a meaningful combination of the components of an ensemble are presented and tested together with the existing ones in order to identify the best performing method in the applications of these models. © Springer-Verlag Berlin Heidelberg 2007.

M3 - Conference contribution

SN - 9783540730064

VL - 4507

SP - 235

EP - 243

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.

PB - Springer Nature

T2 - 9th International Work-Conference on Artificial Neural Networks, IWANN 2007

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ER -

ViSOM ensembles for visualization and classification

Abstract

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