Multiple instance learning with radial basis function neural networks

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A.

Editors: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S. and Parui, S.K.

http://www.informatik.uni-trier.de/~ley/db/conf/iconip/iconip2004.html

Journal: ICONIP

Volume: 3316

Pages: 440-445

Publisher: Springer

DOI: 10.1007/978-3-540-30499-9_67

This data was imported from Scopus:

Authors: Bouchachia, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 3316

Pages: 440-445

eISSN: 1611-3349

ISSN: 0302-9743

This paper investigates the application of radial basis function neural networks (RBFNs) for solving the problem of multiple instance learning (MIL). As a particular form of the traditional supervised learning paradigm, MIL deals with the classification of patterns grouped into bags. Labels of bags are known but not those of individual patterns. To solve the MIL problem, a neural solution based on RBFNs is proposed. A classical application of RBFNs and bag unit-based variants are discussed. The evaluation, conducted on two benchmark data sets, showed that the proposed bag unit-based variant performs very well. © Springer-Verlag Berlin Heidelberg 2004.

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