Publications
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Semi-supervised learning by search of optimal target vector Ghent University
Semi-supervised learning in network-structured data via total variation minimization Ghent University
Spectral-spatial classification of hyperspectral images with semi-supervised graph learning Ghent University
Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection Vrije Universiteit Brussel
This paper studies the classification problem of hyperspectral image (HSI). Inspired by the great success of deep neural networks in Artificial Intelligence (AI), researchers have proposed different deep learning based algorithms to improve the performance of hyperspectral classification. However, deep learning based algorithms always require a large-scale annotated dataset to provide sufficient training. To address this problem, we propose a ...
Scalable semi-supervised Kernel spectral learning using random Fourier Features KU Leuven
© 2016 IEEE. We live in the era of big data with dataset sizes growing steadily over the past decades. In addition, obtaining expert labels for all the instances is time-consuming and in many cases may not even be possible. This necessitates the development of advanced semi-supervised models that can learn from both labeled and unlabeled data points and also scale at worst linearly with the number of examples. In the context of kernel based ...
Large scale semi-supervised learning using KSC based model KU Leuven
© 2014 IEEE. Often in practice one deals with a large amount of unlabeled data, while the fraction of labeled data points will typically be small. Therefore one prefers to apply a semi-supervised algorithm, which uses both labeled and unlabeled data points in the learning process, to have a better performance. Considering the large amount of unlabeled data, making a semi-supervised algorithm scalable is an important task. In this paper we adopt ...
Semi-Supervised and Explainable Machine Learning with an Application to the Low-Voltage Grid KU Leuven
Due to the enormous increase in available data and the computational power needed to process it, interest in the use of machine learning (ML) keeps increasing as it is able to extract valuable insights from this data. In an effort to make it easier to use ML in practice, this thesis proposes several novel ML techniques driven by two industry needs: (1) to make machine learning systems understand what the user wants and (2) to make the user ...
Online semi-supervised learning for motor imagery EEG classification KU Leuven
OBJECTIVE: Time-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to integrate readily available, as well as informative, unlabeled data online, whereas this approach is less investigated. APPROACH: We proposed an online semi-supervised learning scheme to improve the ...
A semi-supervised learning approach towards automatic wireless technology recognition University of Antwerp Ghent University
Radio spectrum has become a scarce commodity due to the advent of several non-collaborative radio technologies that share the same spectrum. Recognizing a radio technology that accesses the spectrum is fundamental to define spectrum management policies to mitigate interference. State-of-the-art approaches for technology recognition using machine learning are based on supervised learning, which requires an extensive labeled data set to perform ...