Searched for: contributor:"Loog, M. (mentor)"
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Li, Mengze (author)
Active learning has the potential to reduce labeling costs in terms of time and money. In practical use, active learning works as an efficient data labeling strategy. Another point of view to look at active learning is to consider active learning as a learning problem, where the training data is queried by the active learner. Under this...
master thesis 2020
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Haas, Joey (author), Klazinga, Rembrandt (author), van Stijn, Nick (author), Teunissen, Jasper (author), Zhang, Peter (author)
The core challenge of the BedBasedEcho BEP project is to create an algorithm to find the heart, and apply it on a robotic echocardiography solution. The team has found multiple complex solutions that are related to this problem, and has extracted useful information from these solutions to apply to this problem. However, some of these complex...
bachelor thesis 2020
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Esseveld, J.R. (author)
Records from ledgers of Dutch companies all across the Netherlands are used in this study. Records can be submitted in the ledgers with various lags, because the data of many different bookkeepers is involved with different workflows. Bookkeepers can be punctual or late, therefore records can be submitted with various lags in the ledgers. This...
master thesis 2020
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Gabriel, Luka (author)
Both MRI and CT imaging are commonly used and combined in medical imaging because of their complementary information about soft tissue and bone respectively. However, CT imaging relies on harmful ionizing radiation. Thus medical imaging scientists are working on transferring harmless MRI scans to CT-like images where using deep learning methods...
master thesis 2018
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Razoux Schultz, Lex (author)
Automated Sentiment Classification (SC) on short text fragments has been an upcoming field of research. Different machine learning techniques and word representation models have proven to be successful in classifying sentiment of opinion expressions in various domains, i.e. different topics or source media. However, when training on a source...
master thesis 2018
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Zhou, Yuan (author)
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master thesis 2017
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van Dorth, Matthijs (author)
master thesis 2017
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Boon, K. (author)
In this thesis I develop a Machine Learning method to combine galactic redshift estimates of previous authors into an aggregate estimate. I investigate weather combining earlier results into a single estimate is a worthwhile effort. Disagreement between the earlier results is used as a metric the quality of estimation. This disagreement is...
master thesis 2017
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Luktuke, Y.Y. (author)
master thesis 2017
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Pals, T.E. (author)
This paper evaluates a method that improves segmentation e ciency by intelligently suggesting planes where correction is most valuable. An existing method is extended to work for segmentation of multiple bones simultaneously. This method is evaluated because in clinical practice it is often necessary that scans are segmented very accurate. When...
master thesis 2017
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Sloots, J.J. (author)
Machine learning approaches are increasingly successful in medical image analysis. Still, learning from MR images poses some serious challenges. Scanner-dependent characteristics effect feature representations directly and hamper the clinical implementation of otherwise successful supervised-learning techniques. To compensate for variations in...
master thesis 2016
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van Rosmalen, N.C. (author)
To possess a computer algorithm that can perform the popular task of object localization with only weak supervision is valuable for numerous reasons. Often enough a certain localization task (e.g. bird localization) simply does not have properly annotated training data available. In this thesis a novel approach called Positive Class Localization...
master thesis 2016
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Viering, T.J. (author)
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant. This is problematic if one wants to train accurate (supervised) predictive models. The main idea behind active learning is that models can perform better with less labeled data, if the model may choose the data from which it learns. Active...
master thesis 2016
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Van der Laan, T.A. (author)
The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with Watkins Q learning. They introduce deep Q networks ...
master thesis 2015
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Runia, T.F.H. (author)
In this thesis we design, implement and study a high-speed object detection framework. Our baseline detector uses integral channel features as object representation and AdaBoost as supervised learning algorithm. We suggest the implementation of two approximation techniques for speeding up the baseline detector and show their effectiveness by...
master thesis 2015
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Van Tulder, G. (author)
Recent advances in importance-weighted active learning solve many of the problems of traditional active learning strategies. But does importance-weighted active learning also produce a reusable sample selection? This thesis explains why reusability can be a problem, how importance-weighted active learning removes some of the barriers to...
master thesis 2012
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Krijthe, J.H. (author)
In order to choose from the large number of classification methods available for use, cross-validation error estimates are often employed. We present this cross-validation selection strategy in the framework of meta-learning and show that conceptually, meta-learning techniques could provide better classifier selections than traditional cross...
master thesis 2012
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Smeelen, M.A. (author)
Nowadays, for the defense and security community, it is of prior importance to classify threats that are merged in a background while at the same time understanding the context of the entire scene. Traditional TV and Infra-Red (IR) cameras allow for an easy context understanding by providing valuable background and scenery information....
master thesis 2012
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Van Giessen, A. (author)
Alzheimer's disease (AD) is a type of dementia which is difficult to diagnose based on clinical observations. Many automated classification algorithms are being developed to aid in the diagnosis. In such algorithms, principal components analysis (PCA) is a popular tool to reduce the dimension of data, get rid of noise and redundancy and thereby...
master thesis 2012
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Van de Sande, J.J.M. (author)
Undesired, human behavior in public environments is an increasing issue in today’s society. The overload of security operators and law enforcement addresses the need for automatic detection of anomalous behavior. The EU-project ADABTS aims to facilitate the protection of EU citizens, property and infrastructure against threats of terrorism,...
master thesis 2012
Searched for: contributor:"Loog, M. (mentor)"
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