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Morette, N. (author), Castro Heredia, L.C. (author), Ditchi, Thierry (author), Mor, A. R. (author), Oussar, Y. (author)
This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted...
journal article 2020
Krijthe, J.H. (author), Loog, M. (author)
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure proposed in this work does not rely on...
journal article 2017
Pastor Serrano, O. (author), Lathouwers, D. (author), Perko, Z. (author)
Background and objective: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical...
journal article 2021