Essays about: "error metrics"
Showing result 1 - 5 of 189 essays containing the words error metrics.
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1. Learning a Grasp Prediction Model for Forestry Applications
University essay from Umeå universitet/Institutionen för fysikAbstract : Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. READ MORE
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2. Effectiveness of Iterative Algorithms for Recovering Phase in the Presence of Noise for Coherent Diffractive Imaging
University essay from Göteborgs universitet / Institutionen för fysikAbstract : Methods of coherent diffractive imaging (CDI) rely on iterative algorithms to reconstruct the complex exit-surface wave (ESW) of the object being imaged from the measured diffraction intensity only. In this thesis we investigate by simulation the artifacts on reconstruction when noise are present in the measurement. READ MORE
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3. Audio Anomaly Detection in Cars
University essay from Göteborgs universitet/Institutionen för matematiska vetenskaperAbstract : Audio anomaly detection in the context of car driving is a crucial task for ensuring vehicle safety and identifying potential faults. This paper aims to investigate and compare different methods for unsupervised audio anomaly detection using a data set consisting of recorded audio data from fault injections and normal "no fault" driving. READ MORE
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4. On The Evaluation of District Heating Load Predictions
University essay from Lunds universitet/Institutionen för energivetenskaperAbstract : District Heating is a technology with the potential to enable a fossil-free society. However, to realize this potential, some improvements need to be made in order to improve District Heating operation at large, decrease losses in the systems, and thus increase the competitiveness of District Heating as a technology. READ MORE
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5. MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels in Stacking Ensemble Learning
University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Abstract : Stacking, also known as stacked generalization, is a method of ensemble learning where multiple base models are trained on the same dataset, and their predictions are used as input for one or more metamodels in an extra layer. This technique can lead to improved performance compared to single layer ensembles, but often requires a time-consuming trial-and-error process. READ MORE