Discovery Science [electronic resource] : 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings / edited by Petra Kralj Novak, Tomislav Šmuc, Sašo Džeroski.
Contributor(s): Kralj Novak, Petra [editor.] | Šmuc, Tomislav [editor.] | Džeroski, Sašo [editor.] | SpringerLink (Online service).
Material type: BookSeries: Lecture Notes in Artificial Intelligence: 11828Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XXII, 546 p. 222 illus., 148 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030337780.Subject(s): Artificial intelligence | Data mining | Computer science | Image processing -- Digital techniques | Computer vision | Software engineering | Artificial Intelligence | Data Mining and Knowledge Discovery | Theory of Computation | Computer Imaging, Vision, Pattern Recognition and Graphics | Software EngineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access onlineAdvanced Machine Learning -- Applications -- Data and Knowledge Representation -- Feature Importance -- Interpretable Machine Learning -- Networks -- Pattern Discovery -- Time Series.
This book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019. The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.
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