Boosting unsupervised competitive learning ensembles

Emilio Corchado, Bruno Baruque, Hujun Yin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping models with some basic classifier ensemble and boosting techniques in order to increase the stability conditions and, as an extension, the classification capabilities of the former. A study and comparison of the performance of some novel and classical ensemble techniques are presented in this paper to test their suitability, both in the fields of data visualization and classification when combined with topology preserving models such as the SOM, ViSOM or ML-SIM. © Springer-Verlag Berlin Heidelberg 2007.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Number of pages9
    ISBN (Print)9783540746898
    Publication statusPublished - 2007
    Event17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto
    Duration: 1 Jul 2007 → …


    Conference17th International Conference on Artificial Neural Networks, ICANN 2007
    Period1/07/07 → …


    • Bagging
    • Boosting
    • Topology preserving mappings
    • Unsupervised learning


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