Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part III
1. Auflage 2020
ISBN: 978-3-030-46132-4
Verlag: Springer International Publishing

Buch, Englisch, Band 11908, 804 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1235 g

Reihe: Lecture Notes in Computer Science

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 Würzburg, 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.

Zielgruppe

Research

Autoren/Hrsg.

Brefeld, Ulf

Fromont, Elisa

Robardet, Céline

Knobbe, Arno

Hotho, Andreas

Maathuis, Marloes

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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.