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

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

ISBN-10:
3030461467
ISBN-13:
9783030461461
Pub. Date:
05/01/2020
Publisher:
Springer International Publishing
ISBN-10:
3030461467
ISBN-13:
9783030461461
Pub. Date:
05/01/2020
Publisher:
Springer International Publishing
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II

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

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Overview

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.

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.


Product Details

ISBN-13: 9783030461461
Publisher: Springer International Publishing
Publication date: 05/01/2020
Series: Lecture Notes in Computer Science , #11907
Edition description: 1st ed. 2020
Pages: 732
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

Supervised Learning.- Multi-Label Learning.- Large-Scale Learning.- Deep Learning.- Probabilistic Models.- Natural Language Processing.
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