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.
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.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, W�rzburg, Germany, September 16-20, 2019, Proceedings, Part II
732
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, W�rzburg, Germany, September 16-20, 2019, Proceedings, Part II
732Paperback(1st ed. 2020)
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) |