On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing (Unknown language)

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In this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing
Fan, Chaoran / Hauser, Helwig | 2019
7
Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications
Janik, Adrianna / Sankaran, Kris / Ortiz, Anthony | 2019
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Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves
Silva, Carla / d'Orey, Pedro M. / Aguiar, Ana | 2019
19
Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks
Hamid, Sagad / Derstroff, Adrian / Klemm, Sören / Ngo, Quynh Quang / Jiang, Xiaoyi / Linsen, Lars | 2019
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