On Unsupervised Learning of Traversal Cost and Terrain Types Identification Using Self-organizing Maps-Reference-Cited by-同舟云学术

On Unsupervised Learning of Traversal Cost and Terrain Types Identification Using Self-organizing Maps

Published:2019 Issue: Volume: Page:654-668
ISSN:0302-9743
Container-title:Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation
language:
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Author:

Faigl Jan^ORCID,Prágr Miloš^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-30487-4_50

Reference18 articles.

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3. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (1979). https://doi.org/10.1109/TPAMI.1979.4766909

4. Faigl, J., Prágr, M.: Incremental traversability assessment learning using growing neural gas algorithm. In: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, pp. 166–176 (2020). https://doi.org/10.1007/978-3-030-19642-4_17

5. Faigl, J., Čížek, P.: Adaptive locomotion control of hexapod walking robot for traversing rough terrains with position feedback only. Robot. Auton. Syst. 116, 136–147 (2019). https://doi.org/10.1016/j.robot.2019.03.008

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