A Mixed Ensemble Approach for the Semi-supervised Problem View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2002

AUTHORS

Evgenia Dimitriadou , Andreas Weingessel , Kurt Hornik

ABSTRACT

In this paper we introduce a mixed approach for the semi-supervised data problem. Our approach consists of an ensemble unsupervised learning part where the labeled and unlabeled points are segmented into clusters. Continuing, we take advantage of the a priori information of the labeled points to assign classes to clusters and proceed to predicting with the ensemble method new incoming ones. Thus, we can finally conclude classifying new data points according to the segmentation of the whole set and the association of its clusters to the classes. More... »

PAGES

571-576

References to SciGraph publications

  • 2001-03. Soft Margins for AdaBoost in MACHINE LEARNING
  • 2001. Boosting Mixture Models for Semi-supervised Learning in ARTIFICIAL NEURAL NETWORKS — ICANN 2001
  • 1998-06. A Tutorial on Support Vector Machines for Pattern Recognition in DATA MINING AND KNOWLEDGE DISCOVERY
  • 1992-01. Self-organizing neural network that discovers surfaces in random-dot stereograms in NATURE
  • Book

    TITLE

    Artificial Neural Networks — ICANN 2002

    ISBN

    978-3-540-44074-1
    978-3-540-46084-8

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-46084-5_93

    DOI

    http://dx.doi.org/10.1007/3-540-46084-5_93

    DIMENSIONS

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