Search for dissertations about: "ensemble learning"

Showing result 1 - 5 of 57 swedish dissertations containing the words ensemble learning.

  2. 1. Visual Analytics for Explainable and Trustworthy Machine Learning

    Author : Angelos Chatzimparmpas; Andreas Kerren; Rafael M. Martins; Ilir Jusufi; Alex Endert; Linnéuniversitetet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; visualization; interaction; visual analytics; explainable machine learning; XAI; trustworthy machine learning; ensemble learning; dimensionality reduction; supervised learning; unsupervised learning; ML; AI; tabular data; visualisering; interaktion; visuell analys; förklarlig maskininlärning; XAI; pålitlig maskininlärning; ensembleinlärning; dimensionesreducering; övervakad inlärning; oövervakad inlärning; ML; AI; tabelldata; Computer Science; Datavetenskap; Informations- och programvisualisering; Information and software visualization;

    Abstract : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. READ MORE

  4. 2. Designing for Adaptable Learning

    Author : Amir Haj-Bolouri; Lars Svensson; Thomas Winman; Per Flensburg; Högskolan Väst; []
    Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Design; action design research; design science research; information systems; integration work; civic orientation; work-integrated learning; e-learning; adaptable learning; Work Integrated Learning; Arbetsintegrerat lärande; Informatik; Informatics;

    Abstract : The research in this thesis emphasizes the endeavor of designing for adaptable learning. Designing for adaptable learning is understood as an overall response to designing for integration work. Designing for integration work is thus classified as a special case of designing for adaptable learning. READ MORE

  5. 3. Automated Malware Detection and Classification Using Supervised Learning

    Author : Raja Muhammad Khurram Shahzad; Niklas Lavesson; Martin Boldt; Welf Löwe; Blekinge Tekniska Högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Malware Detection; Android Malware; Machine Learning; Static Malware Analysis; Cyber Security; Ensemble learning; Supervised Learning; Feature Selection; Computer Science; Datavetenskap;

    Abstract : Malware has been one of the key concerns for Information Technology security researchers for decades. Every year, anti-malware companies release alarming statistics suggesting a continuous increase in the number and types of malware. READ MORE

  6. 4. Utilizing Diversity and Performance Measures for Ensemble Creation

    Author : Tuve Löfström; Högskolan i Skövde; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Ensemble Learning; Machine Learning; Diversity; Artificial Neural Networks; Data Mining; Information Fusion; Computer science; Datavetenskap; Teknik; Technology;

    Abstract : An ensemble is a composite model, aggregating multiple base models into one predictive model. An ensemble prediction, consequently, is a function of all included base models. Both theory and a wealth of empirical studies have established that ensembles are generally more accurate than single predictive models. READ MORE

  7. 5. On Deep Machine Learning Based Techniques for Electric Power Systems

    Author : Ebrahim Balouji; Chalmers tekniska högskola; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep Learning; Cable faults; phase locked loop; Flicker; Harmonics and Interharmonics; Reinforcement learning; Voltage Dip; Active Power filter; Machine Learning; Voltage fluctuation; Partial Discharges;

    Abstract : This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. READ MORE