VizML: A Machine Learning Approach to Visualization Recommendation 論文詳解
本文發表於CHI 2019. 作者來自MIT Media Lab和MIT CSAIL. 簡介與背景 很多可視化工具學習曲線很陡, 往往需要人爲指定: 如寫代碼或各種點擊. 然而, 在初步的數據探索以及創建基本可視化的時候, 往往探索的速度與廣度比人爲指定的定製性更重要. 可視化系統可以利用「數據集的性質會影響它被如何可視化」來支持這些基本需求. 現有的可視化推薦系統分爲兩類: 基於規則的與基於機器
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相關文章
- 1. 論文筆記：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 2. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting論文閱讀
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