Searched for: subject%3A%22Semi%255C-supervised%255C%2Blearning%22
(1 - 16 of 16)
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Slooff, Tom (author)
One of the most potent attacks against cryptographic implementations nowadays is side-channel attacks. Side-channel attacks use unintended leakages in the implementation, for example, electromagnetic radiation, to retrieve the secret key. Over time side-channel attacks have become more powerful, and recently the community has shifted towards...
master thesis 2021
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Lin, Y. (author), Pintea, S. (author), van Gemert, J.C. (author)
Current work on lane detection relies on large manually annotated datasets. We reduce the dependency on annotations by leveraging massive cheaply available unlabelled data. We propose a novel loss function exploiting geometric knowledge of lanes in Hough space, where a lane can be identified as a local maximum. By splitting lanes into separate...
conference paper 2021
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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
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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
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Smalbil, Jos (author)
In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic activity codes in the Dutch business register are less accurate for small enterprises since small enterprises are not labelled manually. To increase the quality of...
master thesis 2020
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Mey, A. (author)
The goal of this thesis is to investigate theoretical results in the field of semi-supervised learning, while also linking them to problems in related subjects as class probability estimation.<br/>
doctoral thesis 2020
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Das, Bishwadeep (author), Isufi, E. (author), Leus, G.J.T. (author)
Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated randomly or using heuristics. The first sometimes leads to unsatisfactory results because...
conference paper 2020
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Mey, A. (author), Viering, T.J. (author), Loog, M. (author)
Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in...
conference paper 2020
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Das, Bishwadeep (author)
In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal...
master thesis 2019
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Jurasiński, Karol (author)
Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled data, which allowed the development of SSL models with the usage of deep neural networks. However, some of these models rely on ad-hoc loss additions...
master thesis 2019
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Bertazzi, Andrea (author)
Semi-supervised algorithms have been shown to possibly have a worse performance than the corresponding supervised model. This may be due to a violation of the assumptions on the data that are introduced in most classification systems. We study an approach that was previously shown to have guarantees of improvement for the LDA classifier in terms...
master thesis 2018
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Mandersloot, Jeroen (author)
Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which instances are assumed to be independent. In this thesis we propose an algorithm that is designed for temporal data. Specifically, we are interested in data with temporal...
master thesis 2018
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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
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Loog, M. (author)
Improvement guarantees for semi-supervised classifiers can currently only be given under restrictive conditions on the data. We propose a general way to perform semi-supervised parameter estimation for likelihood-based classifiers for which, on the full training set, the estimates are never worse than the supervised solution in terms of the log...
journal article 2016
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Hajizadeh, S. (author), Nunez, Alfredo (author), Tax, D.M.J. (author)
Rail defect detection by video cameras has recently gained much attention in both<br/>academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning techniques. In this paper we investigate if positive...
conference paper 2016
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Jamshidi, A. (author), Faghih Roohi, S. (author), Nunez, Alfredo (author), Babuska, R. (author), De Schutter, B.H.K. (author), Dollevoet, R.P.B.J. (author), Li, Z. (author)
This paper develops a defect-based risk analysis methodology for estimating rail failure risk. The methodology relies on an evolution model addressing the severity level of rail surface defect, called squat. The risk of rail failure is assessed by analyzing squat failure probability using a probabilistic analysis of the squat cracks. For this...
conference paper 2016
Searched for: subject%3A%22Semi%255C-supervised%255C%2Blearning%22
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