Search Results - Lecture Notes in Computer Science

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  1. 1

    ArcheryVis: A tool for analyzing and visualizing archery performance data

    Cheng, Z., Li, Z., Luo, Z., Liu, M., D`Alonzo, J., Wang, C.
    Published in Advances in Visual Computing (2023)
    “…Lecture Notes in Computer Science; 14361…”
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  2. 2

    Where will they go? Predicting fine-grained adversarial multi-agent motion using conditional variational autoencoders

    Felsen, P., Lucey, P., Ganguly, S. Collective title: “…Computer Vision - ECCV 2018. Lecture Notes in Computer Science: 15th European Conference, Munich, Germany, September 8-14, 2018…”
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  3. 3

    Drowning in a tsunami of online resources? Time to take stock and re-invent

    Waller, B., Pasanen, K.
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  4. 4

    Automatic badminton action recognition using CNN with adaptive feature extraction on sensor data

    Wang, Y., Fang, W., Ma, J., Li, X., Zhong, A. “…Lecture Notes in Computer Science, 11643…”
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  5. 5

    An asymmetric modeling for action assessment

    Gao, J., Zheng, W.-S., Pan, J.-H., Gao, C., Wang, Y., Zeng, W., Lai, J. Collective title: “…Computer Vision - ECCV 2020. Lecture Notes in Computer Science…”
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  6. 6

    Case-Based Reasoning Research and Development. 28th International Conference, ICCBR 2020, Salamanca, Spain, June 8-12, 2020, Proceedings

    I. Watson, R. Weber
    Published 2020
    “…Lecture Notes in Computer Science, 12311…”
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  7. 7

    Predicting the personal-best times of speed skaters using case-based reasoning

    Smyth, B., Willemsen, M. C. “…Lecture Notes in Computer Science, 12311…”
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  8. 8

    Using case-based reasoning to predict marathon performance and recommend tailored training plans

    Feely, C., Caulfield, B., Lawlor, A., Smyth, B. “…Lecture Notes in Computer Science, 12311…”
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  9. 9

    Quantitative analysis of hand movement in badminton

    Jacob, A., Zakaria, W. N. W., Tomari, M. R. B. M.
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  10. 10

    Distinguishing between roles of football players in play-by-play match event data

    Aalbers, B., van Haaren, J. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  11. 11

    Player valuation in European football

    Nsolo, E., Lambrix, P., Carlsson, N. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  12. 12

    Ranking the teams in European football leagues with agony

    Neumann, S., Ritter, J., Budhathoki, K. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  13. 13

    Interpreting deep sports analytics: Valuing actions and players in the NHL

    Liu, G., Zhu, W., Schulte, O. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  14. 14

    Player pairs valuation in ice hockey

    Ljung, D., Carlsson, N., Lambrix, P. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  15. 15

    Model trees for identifying exceptional players in the NHL and NBA drafts

    Liu, V., Schulte, O., Li, C. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  16. 16

    Real-time power performance prediction in Tour de France

    Kataoka, Y., Gray, P. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  17. 17

    Predicting pass receiver in football using distance based features

    Dauxais, Y., Gautrais, C. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  18. 18

    Football pass prediction using player locations

    Fournier-Viger, P., Liu, T., Chun-Wei Lin, J. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  19. 19

    Deep learning from spatial relations for soccer pass prediction

    Hubácek, O., Šourek, G., Železný, F. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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  20. 20

    Predicting the receivers of football passes

    Li, H., Zhang, Z. Collective title: “…Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330…”
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