Abstract
Transductive learning is the problem of designing learning machines that succesfully generalize only on a given set of input patterns. In this paper we begin the study towards the extension of Extreme Learning Machine (ELM) theory to the transductive setting, focusing on the binary classification case. To this end, we analyze previous work on Transductive Support Vector Machines (TSVM) learning, and introduce the Transductive ELM (TELM) model. Contrary to TSVM, we show that the optimization of TELM results in a purely combinatorial search over the unknown labels. Some preliminary results on an artifical dataset show substained improvements with respect to a standard ELM model.
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© 2014 Springer International Publishing Switzerland
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Scardapane, S., Comminiello, D., Scarpiniti, M., Uncini, A. (2014). A Preliminary Study on Transductive Extreme Learning Machines. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_3
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DOI: https://doi.org/10.1007/978-3-319-04129-2_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
Online ISBN: 978-3-319-04129-2
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