Sökning: "Machine learning"

Visar resultat 1 - 5 av 489 avhandlingar innehållade orden Machine learning.

  1. 1. Approaches to Interactive Online Machine Learning

    Författare :Agnes Tegen; Paul Davidsson; Jan A. Persson; Henrik Boström; Malmö universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Interactive Machine Learning; Online Learning; Active Learning; Machine Teaching;

    Sammanfattning : With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. LÄS MER

  3. 2. Modularization of the Learning Architecture : Supporting Learning Theories by Learning Technologies

    Författare :Fredrik Paulsson; Yngve Sundblad; Miguel Angel-Cecilia; KTH; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Computer Science; Technology Enhanced Learning; e-learning; Semantic Web; Service Orientation; Learning Object; Virtual Learning Environment; Computer science; Datavetenskap;

    Sammanfattning : This thesis explores the role of modularity for achieving a better adaptation of learning technology to pedagogical requirements. In order to examine the interrelations that occur between pedagogy and computer science, a theoretical framework rooted in both fields is applied. LÄS MER

  4. 3. Protein Model Quality Assessment : A Machine Learning Approach

    Författare :Karolis Uziela; Arne Elofsson; Liam McGuffin; Stockholms universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Protein Model Quality Assessment; structural bioinformatics; machine learning; deep learning; support vector machine; proq; Artificial Neural Network; protein structure prediction; biokemi med inriktning mot bioinformatik; Biochemistry towards Bioinformatics;

    Sammanfattning : Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). LÄS MER

  5. 4. Applied Machine Learning in Steel Process Engineering : Using Supervised Machine Learning Models to Predict the Electrical Energy Consumption of Electric Arc Furnaces

    Författare :Leo Carlsson; Pär Jönsson; Peter Samuelsson; Mikael Vejdemo-Johansson; Henrik Saxen; KTH; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Electric Arc Furnace; Electrical Energy Consumption; Statistical Modelling; Machine Learning; Interpretable Machine Learning; Predictive Modelling; Industry 4.0; Ljusbågsugn; Elenergiförbrukning; Statistisk Modellering; Maskininlärning; Tolkningsbar Maskininlärning; Prediktiv Modellering; Industri 4.0; Materials Science and Engineering; Teknisk materialvetenskap; Metallurgical process science; Metallurgisk processvetenskap;

    Sammanfattning : The steel industry is in constant need of improving its production processes. This is partly due to increasing competition and partly due to environmental concerns. One commonly used method for improving these processes is through the act of modeling. LÄS MER

  7. 5. Energy Efficiency in Machine Learning : Approaches to Sustainable Data Stream Mining

    Författare :Eva García Martín; Håkan Grahn; Veselka Boeva; Emiliano Casalicchio; Jesse Read; Blekinge Tekniska Högskola; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; machine learning; energy efficiency; data stream mining; green machine learning; edge computing; Computer Science; Datavetenskap;

    Sammanfattning : Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability. LÄS MER