Machine Learning and Knowledge Discovery in Databases (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
732
Utgivningsdatum
2020-05-02
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Fromont, Elisa / Hotho, Andreas
Illustrationer
191 Illustrations, color; 123 Illustrations, black and white; XXVI, 732 p. 314 illus., 191 illus. in
Dimensioner
234 x 156 x 38 mm
Vikt
1044 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030461461

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2019, Wrzburg, Germany, September 1620, 2019, Proceedings, Part II

Häftad,  Engelska, 2020-05-02
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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 Wrzburg, 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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Innehållsförteckning

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