Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
Cover of the book Machine Learning and Knowledge Discovery in Databases by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783030109288
Publisher: Springer International Publishing Publication: January 22, 2019
Imprint: Springer Language: English
Author:
ISBN: 9783030109288
Publisher: Springer International Publishing
Publication: January 22, 2019
Imprint: Springer
Language: English

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. 

The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. 

The contributions were organized in topical sections named as follows:
Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation.
Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. 
Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. 

The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. 

The contributions were organized in topical sections named as follows:
Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation.
Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. 
Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

More books from Springer International Publishing

Cover of the book Looking Back on President Barack Obama’s Legacy by
Cover of the book Automotive Systems Engineering II by
Cover of the book The History of Business in Africa by
Cover of the book Nanopackaging: From Nanomaterials to the Atomic Scale by
Cover of the book Social Exclusion by
Cover of the book The Seduction of Fiction by
Cover of the book Boundaries within: Nation, Kinship and Identity among Migrants and Minorities by
Cover of the book WELL-BEING by
Cover of the book Digital Geoarchaeology by
Cover of the book Space Engineering by
Cover of the book If the Universe Is Teeming with Aliens ... WHERE IS EVERYBODY? by
Cover of the book Methods of Fourier Analysis and Approximation Theory by
Cover of the book 5G Wireless Systems by
Cover of the book Mathematical Aspects of Computer and Information Sciences by
Cover of the book Religious Cognition in China by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy