document

Deciphering Learning Curve Characteristics via K-Means Clustering of Curve Model Parameters

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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Prevalence of non-monotonicity in learning curves

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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Clustering Learning Curves in Machine Learning using K-Means Algorithm: Can patterns be identified amongst learning curves after the application of the K-Means algorithm using point and statistical vectors?

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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Learning Curve Extrapolation using Machine Learning: Benefits and Limitations of using LCPFN for Learning Curve Extrapolation

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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Learning Curves: How do Data Imbalances affect the Learning Curves using Nearest Mean Model?

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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The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization

Egele, Romain (author), Mohr, F. (author), Viering, T.J. (author), Balaprakash, Prasanna (author)

To reach high performance with deep learning, hyperparameter optimization (HPO) is essential. This process is usually time-consuming due to costly evaluations of neural networks. Early discarding techniques limit the resources granted to unpromising candidates by observing the empirical learning curves and canceling neural network training as...

journal article 2024

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Empirical Investigation of Learning Curves: Assessing Convexity Characteristics

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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The influence of the dimensionality on the parameters of the learning curve model

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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A Comparative Analysis of Learning Curve Models and their Applicability in Different Scenarios: Finding datasets patterns which lead to certain parametric curve model

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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”How Much Data is Enough?” Learning curves for machine learning: Investigating alternatives to the Levenberg-Marquardt algorithm for learning curve extrapolation

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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Non-Monotonicity in Empirical Learning Curves: Identifying non-monotonicity through slope approximations on discrete points

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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Towards a Linear-Data Monotone Wrapper Algorithm For Machine Learning Algorithms

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

document

Hydrogen fuel-cell heavy duty trucks for long haul transport in the EU; conditions for a successful implementation

Misdorp, Pim (author)

It is uncertain which drivetrain will be used for future long haul transport of goods in the EU. One of the possible technologies is the use of hydrogen fuel cell heavy-duty trucks (HFC-HDTs). This research focuses on success and failure factors which influence the successful implementation of HFC-HDTs in the EU and aims to achieve an overview...

master thesis 2023

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On Safety in Machine Learning

Viering, T.J. (author)

This dissertation focuses on safety in machine learning. Our adopted safety notion is related to robustness of learning algorithms. Related to this concept, we touch upon three topics: explainability, active learning and learning curves.<br/><br/>Complex models can often achieve better performance compared to simpler ones. Such larger models are...

doctoral thesis 2023

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Analyzing Components of a Transformer under Different Dataset Scales in 3D Prostate CT Segmentation

Tan, Yicong (author), Mody, P. (author), van der Valk, Viktor (author), Staring, M. (author), van Gemert, J.C. (author)

Literature on medical imaging segmentation claims that hybrid UNet models containing both Transformer and convolutional blocks perform better than purely convolutional UNet models. This recently touted success of hybrid Transformers warrants an investigation into which of its components contribute to its performance. Also, previous work has a...

conference paper 2023

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Also for k-means: more data does not imply better performance

Loog, M. (author), Krijthe, J.H. (author), Bicego, Manuele (author)

Arguably, a desirable feature of a learner is that its performance gets better with an increasing amount of training data, at least in expectation. This issue has received renewed attention in recent years and some curious and surprising findings have been reported on. In essence, these results show that more data does actually not...

journal article 2023

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The Shape of Learning Curves: A Review

Viering, T.J. (author), Loog, M. (author)

Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational complexity of model training and hyperparameter tuning. This review recounts the origins of...

review 2023

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LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks

Mohr, Felix (author), Viering, T.J. (author), Loog, M. (author), van Rijn, Jan N. (author)

The use of learning curves for decision making in supervised machine learning is standard practice, yet understanding of their behavior is rather limited. To facilitate a deepening of our knowledge, we introduce the Learning Curve Database (LCDB), which contains empirical learning curves of 20 classification algorithms on 246 datasets. One of...

conference paper 2023

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To Tune or not to Tune: Hyperparameter Influence on the Learning Curve

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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In Search of Best Learning Curve Model

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