Overview

Editors:

• Plamen Angelov⁰,
• Yannis Manolopoulos¹,
• Lazaros Iliadis²,
• Asim Roy³,
• …
• Marley Vellasco⁴

Show editors

1. Plamen Angelov

   1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom
      

   View editor publications

   You can also search for this editor inPubMed Google Scholar

2. Yannis Manolopoulos

   1. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece
      

   View editor publications

   You can also search for this editor inPubMed Google Scholar

3. Lazaros Iliadis

   1. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece
      

   View editor publications

   You can also search for this editor inPubMed Google Scholar

4. Asim Roy

   1. WPC Information Systems Faculty, Arizona State University , Tempe, USA
      

   View editor publications

   You can also search for this editor inPubMed Google Scholar

5. Marley Vellasco

   1. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Brazil
      

   View editor publications

   You can also search for this editor inPubMed Google Scholar

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.

• 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

Other volumes

• 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

Policies and ethics