Searched for: contributor%3A%22Loog%2C+M.+%28mentor%29%22
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Nguyen, Dean (author)
Learning curves have been used extensively to analyse learners' behaviour and practical tasks such as model selection, speeding up training and tuning models. Nonetheless, we still have a relatively limited understanding of the behaviour of learning curves themselves, in particular, whether there exists a parametric function that can best model...
bachelor thesis 2022
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KIM, DONGHWI (author)
Extrapolation of the learning curve provides an estimation of how much data is needed to achieve the desired performance. It can be beneficial when gathering data is complex, or computation resource is limited. One of the essential processes of learning curve extrapolation is curve fitting. This research first analyses the behaviour of existing...
bachelor thesis 2022
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Bhaskaran, Prajit (author)
A learning curve displays the measure of accuracy/error on test data of a machine learning algorithm trained on different amounts of training data. They can be modeled by parametric curve models that help predict accuracy improvement through curve extrapolation methods. However, these learning curves have only been mainly generated from default...
bachelor thesis 2022
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Chen, Zhiyi (author)
The learning curve illustrates how the generalization performance of the learner evolves with more training data. It can predict the amount of data needed for decent accuracy and the highest achievable accuracy. However, the behavior of learning curves is not well understood. Many assume that the more training data provided, the better the...
bachelor thesis 2022
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Klazinga, Rembrandt (author)
Autoencoders seek to encode their input into a bottleneck of latent neurons, and then decode it to reconstruct the input. However, if the input data has an intrinsic dimension (ID) smaller than the number of latent neurons in the bottleneck, this encoding becomes redundant. <br/>In this paper, we study using the Early-Bird (EB) technique, a...
master thesis 2022
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Bui, NAM THANG (author)
Although there are many promising applications of a learning curve in machine learning, such as model selection, we still know very little about what factors influence their behaviours. The aim is to study the impact of the inherent characteristics of the datasets on the learning shapes, which are noise, discretized input and dimensionality. We...
bachelor thesis 2022
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Huang, Wenxuan (author)
Supervised machine learning is a growing assistive framework for professional decision-making. Yet bias that causes unfair discrimination has already been presented in the datasets. This research proposes a method to reduce model unfairness during the machine learning training process without altering the sample value or the prediction value....
master thesis 2022
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Biesheuvel, Julian (author)
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...
bachelor thesis 2021
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Lamon, Julien (author)
With an expectation of 8.3 trillion photos stored in 2021 [1], convolutional neural networks (CNN) are beginning to be preeminent in the field of image recognition. However, with this deep neural network (DNN) still being seen as a black box, it is hard to fully employ its capabilities. A need to tune hyperparameters is required to have a robust...
bachelor thesis 2021
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Thakoersingh, Ratish (author)
This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to...
bachelor thesis 2021
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den Heijer, Remco (author)
Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...
bachelor thesis 2021
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Bons, Wouther (author)
Currently, trained machine learning models are readily available, but their training data might not be (for example due to privacy reasons). This thesis investigates how pre-trained models can be combined for performance on all their source domains, without access to data. This problem is formulated as a Multiple-Source Domain Adaptation (MSA)...
master thesis 2021
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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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van Tussenbroek, Thomas (author)
Authorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature...
bachelor thesis 2020
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Keukeleire, Pia (author)
In recent years many new text generation models have been developed while evaluation of text generation remains a considerable challenge.  Currently, the only metric that is able to fully capture the quality of a generated text is human evaluation, which is expensive and time consuming. One of the most used intrinsic...
bachelor thesis 2020
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Happel, David (author)
Using transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF...
bachelor thesis 2020
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Schmahl, Katja (author)
Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain a stereotypical gender bias. This unwanted bias is then also present in the word embeddings and in downstream applications in the field of natural language processing. To prevent and reduce this, more knowledge about the gender bias is...
bachelor thesis 2020
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Raijmakers, Thijs (author)
Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, &amp; Lin, 2015) and résumé parsing (Nasser, Sreejith, &amp; Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such...
bachelor thesis 2020
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