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New Frontiers in Mining Complex Patterns

E-BookPDF1 - PDF WatermarkE-Book
155 Seiten
Englisch
Springer International Publishingerschienen am13.05.20201st ed. 2020
This book constitutes the refereed post-conference proceedings of the 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with ECML-PKDD 2019 in Würzburg, Germany, in September 2019. The workshop focused on the latest developments in the analysis of complex and massive data sources, such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
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BuchKartoniert, Paperback
EUR51,36
E-BookPDF1 - PDF WatermarkE-Book
EUR50,28
E-BookPDF1 - PDF WatermarkE-Book
EUR50,28
E-BookPDF1 - PDF WatermarkE-Book
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
EUR48,14

Produkt

KlappentextThis book constitutes the refereed post-conference proceedings of the 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with ECML-PKDD 2019 in Würzburg, Germany, in September 2019. The workshop focused on the latest developments in the analysis of complex and massive data sources, such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.
Details
Weitere ISBN/GTIN9783030488611
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2020
Erscheinungsdatum13.05.2020
Auflage1st ed. 2020
ReiheLecture Notes in Artificial Intelligence
Seiten155 Seiten
SpracheEnglisch
IllustrationenXII, 155 p. 111 illus., 33 illus. in color.
Artikel-Nr.5851297
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
A Framework for Pattern Mining and Anomaly Detection in Multi-Dimensional Time Series and Event Logs.- A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality.- Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners.- Neural Hybrid Recommender: Recommendation Needs Collaboration.- Discovering Discriminative Nodes for Classification with Deep Graph Convolutional Methods.- Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams.- Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning.- Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda.- Hough Transform as a Tool for the Classification of Vehicle Speed Changes in on-road Audio Recordings.mehr

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