Name: Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par (en Inglés)
Price: 322772.00 COP
Availability: InStock
Author: Brefeld, Ulf ; Fromont, Elisa ; Hotho, Andreas
ISBN: 9783030461461

portada Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par (en Inglés)

Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Brefeld, Ulf ; Fromont, Elisa ; Hotho, Andreas

Idioma

Inglés

N° páginas

732

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 3.8 cm

Peso

1.04 kg.

ISBN13

9783030461461

Categorías

Ciencia y análisis de datos: generalidades
Análisis y captura de datos
Inteligencia artificial
Interacción persona-ordenador

Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par (en Inglés)

Brefeld, Ulf ; Fromont, Elisa ; Hotho, Andreas (Autor) · Springer · Tapa Blanda

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Reseña del libro "Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par (en Inglés)"

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.