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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115703


    Title: Evaluating Machine Learning Varieties for NBA Players Winning Contribution
    Other Titles: English
    Authors: Hsu, P.;Galsanbadam, S.;Yang, Jr-Syu;Yang, C.
    Keywords: Maching Learning
    Date: 2018-06-28
    Issue Date: 2018-12-25 12:10:30 (UTC+8)
    Abstract: The reputation of NBA breach its boundary worldwide and have numerous fans around all the world. As the league concerns a lot of money and fans, several of researches have been challenged trying to predict its results and winning teams. Through its history a lot of data and statistics are collected for NBA and it’s still becoming more rich and detailed. Even though, such enormous data available, it is still complicated to analyze and predict the outcome of match. In order to achieve exceptional prediction rating we will be focusing on how individual player’s achievement influences the team win rating. For our learning techniques, we choose SVR, polynomial regression and random forest regression as they are able to give consistent result regardless of complex data features.
    Relation: ICSSE 2018
    Appears in Collections:[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Proceeding

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