Sökning: "Relevance Vector Machines"

Hittade 3 uppsatser innehållade orden Relevance Vector Machines.

  1. 1. Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets

    Master-uppsats, Linköpings universitet/Produktionsekonomi

    Författare :George Abo Al Ahad; Abbas Salami; [2018]
    Nyckelord :Machine Learning; Finance; Financial Time Series; Support Vector Machines; Relevance Vector Machines; Multiple Kernel Learning; Simulated Annealing; SVM; RVM; MKL; SA; FSVM; TSVM; FTSVM;

    Sammanfattning : Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. LÄS MER

  3. 2. Relevance classification of connected vehicles for short-lived distributed geospatial events

    Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

    Författare :William Perkola; [2017]
    Nyckelord :;

    Sammanfattning : Continuously increasing connectivity of today’s road vehicles has made communication between road vehicles and the outside world more accessible and easier to handle, which has resulted in newly identified areas of improvement regarding road safety and traffic efficiency. Such an area concerns informing road vehicles about ongoing events in the spatial road network near road vehicles and the problem of determining for which road vehicles such information is relevant in an efficient manner. LÄS MER

  4. 3. Classification of Hate Tweets and Their Reasons using SVM

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för datalogi

    Författare :Natalya Tarasova; [2016]
    Nyckelord :Support Vector Machines; classification; Akaike Information Criteria; machine learning; Twitter; hate tweets;

    Sammanfattning : Denna studie fokuserar på att klassificera hat-meddelanden riktade mot mobiloperatörerna Verizon,  AT&T and Sprint. Huvudsyftet är att med hjälp av maskininlärningsalgoritmen Support Vector Machines (SVM) klassificera meddelanden i fyra kategorier - Hat, Orsak, Explicit och Övrigt - för att kunna identifiera ett hat-meddelande och dess orsak. LÄS MER