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Ozgur, Enes Arda (author)Learning curves illustrate the relationship between the performance of learning algorithms and the increasing volume of training data [1, 2, 3]. While the concept of learning curves is well-established, clustering these curves based on fitting parameters remains an underexplored area. Our study delves into this domain and leverages the Learning...bachelor thesis 2024
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Gafton, Dinu (author)Learning curves are useful to determine the amount of data needed for a certain performance. The conventional belief is that increasing the amount of data improves performance. However, recent work challenges this assumption, and shows nonmonotonic behaviors of certain learners on certain problems. This paper presents a new approach for...bachelor thesis 2024
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Ramsundersingh, Pravesha (author)A learning curve can serve as an indicator of the “performance of trained models versus the training set size” [1]. Recent research on learning curve analysis has been carried out within the Learning Curve Database (LCDB) [2] This paper will investigate if there are similarities amongst these curves by clustering those provided by the LCDB. The...bachelor thesis 2024
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Johari, Pratham (author)This study explores the extrapolation of learning curves, a crucial aspect in evaluating learner performance with varying dataset sample sizes. We use the Learning Curve Prior Fitted Network (LC-PFN), a transformer pre-trained on synthetic data with proficiency in approximate Bayesian inference, to investigate its predictive accuracy using the...bachelor thesis 2024
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Feng, Kevin (author)This research investigates the impact of data imbalances on the learning curve using the nearest mean model. Learning curves are useful to represent the performance of the model as the training size increases. Imbalanced datasets are often encountered in real-life scenarios and pose challenges to standard classifier models impacting their...bachelor thesis 2024
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Gogora, Kristián (author)Nonconvexity in learning curves is almost always undesirable. A machine learning model with a non-convex learning curve either requires a larger quantity of data to observe progress in its accuracy or experiences an exponential decrease of accuracy at low sample sizes, with no improvement in accuracy even when more data points are added. This...bachelor thesis 2023
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Mereuta, Andrei (author)Learning curves in machine learning are graphical representations that depict the relationship between a model's performance and the amount of training data it has been exposed to. They play a fundamental role in obtaining the knowledge and skills across a range of domains. Although there are already quite some researches studying machine...bachelor thesis 2023
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Kalandadze, Anna (author)Learning curves display predictions of the chosen model’s performance for different training set sizes. They can help estimate the amount of data required to achieve a minimal error rate, thus aiding in reducing the cost of data collection. However, our understanding and knowledge of the various shapes of learning curves and their applicability...bachelor thesis 2023
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Negru, Lucian (author)The conducted research explores fitting algorithms for learning curves. Learning curves describe how the performance of a machine learning model changes with the size of the training input. Therefore, fitting these learning curves and extrapolating them can help determine the required data set size for any desired performance. <br/><br/>The...bachelor thesis 2023
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Socol, Codrin (author)Learning curves are used to shape the performance of a Machine Learning (ML) model with respect to the size of the set used for training it. It was commonly thought that adding more training samples would increase the model's accuracy (i.e., they are monotone), but recent works show that may not always be the case. In other words, some learners...bachelor thesis 2023
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Kam, Berend (author)<br/>Machine learning algorithms (learners) are typically expected to produce monotone learning curves, meaning that their performance improves as the size of the training dataset increases. However, it is important to note that this behavior is not universally observed. Recently monotonicity of learning curves has gained renewed attention, as...master thesis 2023
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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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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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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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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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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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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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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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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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