Sökning: "support-vector machines"

Visar resultat 1 - 5 av 54 avhandlingar innehållade orden support-vector machines.

  1. 1. Fixed points, fractals, iterated function systems and generalized support vector machines

    Författare :Xiaomin Qi; Sergei Silvestrov; Anatoliy Malyarenko; Daniel Andren; Viktor Abramov; Mälardalens högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; support vector machine; fixed points; iterated function system; variational inequality; Mathematics Applied Mathematics; matematik tillämpad matematik;

    Sammanfattning : In this thesis, fixed point theory is used to construct a fractal type sets and to solve data classification problem. Fixed point method, which is a beautiful mixture of analysis, topology, and geometry has been revealed as a very powerful and important tool in the study of nonlinear phenomena. LÄS MER

  3. 2. High-Performance Computing For Support Vector Machines

    Författare :Shirin Tavara; Alexander Schliep; Alexander Karlsson; Richard Johansson; Högskolan i Skövde; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Skövde Artificial Intelligence Lab SAIL ; Skövde Artificial Intelligence Lab SAIL ; INF301 Data Science; INF301 Data Science;

    Sammanfattning : Machine learning algorithms are very successful in solving classification and regression problems, however the immense amount of data created by digitalization slows down the training and predicting processes, if solvable at all. High-Performance Computing(HPC) and particularly parallel computing are promising tools for improving the performance of machine learning algorithms in terms of time. LÄS MER

  4. 3. Distributed and federated learning of support vector machines and applications

    Författare :Shirin Tavara; Alexander Schliep; Alexander Karlsson; Lili Jiang; Högskolan i Skövde; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Skövde Artificial Intelligence Lab SAIL ; Skövde Artificial Intelligence Lab SAIL ;

    Sammanfattning : Machine Learning (ML) has achieved remarkable success in solving classification, regression, and related problems over the past decade. In particular the exponential growth of digital data, makes using ML inevitable and necessary to exploit the wealth of information hidden inside the data. LÄS MER

  5. 4. Computational and spatial analyses of rooftops for urban solar energy planning

    Författare :Mohammad Aslani; Stefan Seipel; S. Anders Brandt; Julia Åhlén; Alison Jane Heppenstall; Högskolan i Gävle; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Machine learning; Classification; Segmentation; Support vector machines; Instance selection; Rooftop plane segmentation; Photovoltaic panels; Utiliz-able rooftop areas; Geoinformatics; maskininlärning; klassificering; segmentering; stödvektormaskiner; urval av träningsdata; segmentering av takytor; solcellspaneler; utnyttjande av takytor; geoinformatik; Hållbar stadsutveckling; Sustainable Urban Development;

    Sammanfattning : In cities where land availability is limited, rooftop photovoltaic panels (RPVs) offer high potential for satisfying concentrated urban energy demand by using only rooftop areas. However, accurate estimation of RPVs potential in relation to their spatial distribution is indispensable for successful energy planning. LÄS MER

  7. 5. On Tracing Flicker Sources and Classification of Voltage Disturbances

    Författare :Peter Axelberg; Högskolan i Borås; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; power transmission and distribution; digital signal processing; power quality monitoring; light flicker; power system disturbance; support vector machines; Medicinteknik; voltage dips;

    Sammanfattning : Developments in measurement technology, communication and data storage have resulted in measurement systems that produce large amount of data. Together with the long existing need for characterizing the performance of the power system this has resulted in demand for automatic and efficient information-extraction methods. LÄS MER