Essays about: "klassificering"
Showing result 1 - 5 of 534 essays containing the word klassificering.
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1. Decision Trees for Classification of Repeated Measurements
University essay from Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakultetenAbstract : Classification of data from repeated measurements is useful in various disciplines, for example that of medicine. This thesis explores how classification trees (CART) can be used for classifying repeated measures data. READ MORE
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2. Erratic Patterns : Unravelling the Cultural Transfers of Library Classifiers
University essay from Uppsala universitet/Institutionen för ABMAbstract : This thesis investigates the oft-overlooked influence of the classifier's input during the assignment of library classifications and draws upon cultural transfer theory to shed light on the underlying principles that guide the process. Classifiers' personal knowledge, experience, and beliefs, have a critical role in determining the 'aboutness' of a work and its subsequent classification. READ MORE
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3. Few-Shot Learning for Quality Inspection
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : The goal of this project is to find a suitable Few-Shot Learning (FSL) model that can be used in a fault detection system for use in an industrial setting. A dataset of Printed Circuit Board (PCB) images has been created to train different FSL models. READ MORE
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4. Analyzing How Blended Emotions are Expressed using Machine Learning Methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Blended emotion is a classification of emotional experiences that involve the combination of multiple emotions. Research on the expression of blended emotions allows researchers to understand how different emotions interact and coexist in an individual’s emotional experience. READ MORE
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5. Gender Bias in Machine Learning : The Effect of Using Female Versus Male Audio When Classifying Emotions in Speech Using Machine Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : To avoid discrimination between the genders and to improve the performance of machine learning, it is important to evaluate how different test data can impact how accurate machine learning models can be. This study investigates if the distribution between women and men in the training data affects how accurately different machine learning models can classify emotions used in the speaker’s tone of voice. READ MORE