Editors:
- Reports on the latest neural network technologies for big data analytics
- Presents innovative algorithmic approaches to analyzing big data
- Describes big data analytics applications to solve real-world problems
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 529)
Conference series link(s): INNS: INNS Conference on Big Data
Conference proceedings info: INNS 2016.
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (34 papers)
-
Front Matter
About this book
The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23–25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.
Keywords
- ANNS
- Autonomous, Online, Incremental Learning In Big Data
- Big Data Analytics
- Big Data And Cloud Computing
- Big Data Streams Analytics
- Cognitive Modeling And Big Data
- Deep Neural Network Learning
- Deep Reinforcement Learning
- Evolutionary Systems And Big Data
- Evolving Systems For Big Data Analytics
- Fuzzy Data Analysis
- Information Propagation Analysis
- INNS-BigData 2016
- Learning Algorithms Streaming Data
- Neuromorphic Hardware
- Online Learning
- Online Social Networks
- Recommendation Systems/Collaborative Filtering For Big Data
- Systems Neuroscience
- Scalable Algorithms For Big Data
Editors and Affiliations
-
School of Computing and Communications, Lancaster University , Lancaster, United Kingdom
Plamen Angelov
-
Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece
Yannis Manolopoulos
-
Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece
Lazaros Iliadis
-
WPC Information Systems Faculty, Arizona State University , Tempe, USA
Asim Roy
-
Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Brazil
Marley Vellasco
Bibliographic Information
Book Title: Advances in Big Data
Book Subtitle: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
Editors: Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco
Series Title: Advances in Intelligent Systems and Computing
DOI: https://doi.org/10.1007/978-3-319-47898-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Softcover ISBN: 978-3-319-47897-5Published: 09 October 2016
eBook ISBN: 978-3-319-47898-2Published: 20 October 2016
Series ISSN: 2194-5357
Series E-ISSN: 2194-5365
Edition Number: 1
Number of Pages: XVII, 348
Number of Illustrations: 101 b/w illustrations
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Artificial Intelligence