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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2019, Wurzburg, Germany, September 16-20, 2019, Proceedings, Part II - Ulf Brefeld

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2019, Wurzburg, Germany, September 16-20, 2019, Proceedings, Part II

By: Ulf Brefeld, Elisa Fromont, Andreas Hotho

eText | 1 May 2020 | Edition Number 1

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The three volume proceedings LNAI 11906 - 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Wurzburg, Germany, in September 2019.

The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track.

The contributions were organized in topical sections named as follows:

Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization.

Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing.

Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track.

Chapter "Incorporating Dependencies in Spectral Kernels for Gaussian Processes" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceDatabasesData Capture & AnalysisHuman-Computer InteractionDigital Lifestyle & Online World: Consumer & User GuidesInternet Guides & Online ServicesComputer Networking & CommunicationsInformation Technology General IssueBusiness Applications
Computer Hardware

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