Essays about: "classification robustness"
Showing result 16 - 20 of 55 essays containing the words classification robustness.
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16. Improving Brain Tumor Segmentation using synthetic images from GANs
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : Artificial intelligence (AI) has been seeing a great amount of hype around it for a few years but more so now in the field of diagnostic medical imaging. AI-based diagnoses have shown improvements in detecting the smallest abnormalities present in tumors and lesions. This can tremendously help public healthcare. READ MORE
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17. Commodity Futures Pricing Via Machine Learning: An Empirical Approach
University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomiAbstract : The goal of this thesis is to use established methodologies in the field of machine learning in finance to extend the list of current applications to commodity futures, reviewing and refining the established empirical approaches to return forecasting and hyperparameter optimization. We thus investigate the out of sample predictive accuracy of tree-based machine learning (ML) techniques and neural networks applied to monthly commodity futures returns, relying on conventional regression and classification accuracy metrics. READ MORE
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18. Resource-efficient image segmentation using self-supervision and active learning
University essay from KTH/Skolan för industriell teknik och management (ITM)Abstract : Neural Networks have been demonstrated to perform well in computer vision tasks, especially in the field of semantic segmentation, where a classification is performed on a per pixel-level. Using deep learning can reduce time and effort in comparison to manual segmentation, however, the performance of neural networks highly depends on the data quality and quantity, which is costly and time-consuming to obtain; especially for image segmentation tasks. READ MORE
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19. A Comparative Analysis of Robustness to Noise in Machine Learning Classifiers
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Data that stems from real measurements often to some degree contain distortions. Such distortions are generally referred to as noise in machine learning terminology, and can lead to decreased classification accuracy and poor prediction results. READ MORE
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20. Automatic Categorization of News Articles With Contextualized Language Models
University essay from Linköpings universitet/Artificiell intelligens och integrerade datorsystemAbstract : This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. READ MORE
