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Advances in Big Data
Title statement Advances in Big Data [electronic resource] : Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece / edited by Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco. Publication Cham : Springer International Publishing : Imprint: Springer, 2017. Phys.des. XVII, 348 p. 101 illus. online resource. ISBN 9783319478982 Edition Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 529 Contents Predicting human behavior based on web search activity: Greek referendum of 2015 -- Compact Video Description and Representation for Automated Summarization of Human Activities -- Attribute Learning for Network Intrusion Detection -- A Fast Deep Convolutional Neural Network for face detection in Big Visual Data -- Learning Symbols by Neural Network -- Designing HMMs models in the age of Big Data -- Extended Formulations for Online Action Selection on Big Action Sets -- Multi-Task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports -- An infrastructure and approach for infering knowledge over Big Data in the Vehicle Insurance Industry -- Unified Retrieval Model of Big Data -- Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species. Notes to Availability Přístup pouze pro oprávněné uživatele Another responsib. Angelov, Plamen. Manolopoulos, Yannis. Iliadis, Lazaros. Roy, Asim. Vellasco, Marley. Another responsib. SpringerLink (Online service) Subj. Headings Engineering. * Data mining. * Artificial intelligence. * Computational intelligence. Form, Genre elektronické knihy electronic books Country Německo Language angličtina Document kind Electronic books URL Plný text pro studenty a zaměstnance UPOL 
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.
Predicting human behavior based on web search activity: Greek referendum of 2015 -- Compact Video Description and Representation for Automated Summarization of Human Activities -- Attribute Learning for Network Intrusion Detection -- A Fast Deep Convolutional Neural Network for face detection in Big Visual Data -- Learning Symbols by Neural Network -- Designing HMMs models in the age of Big Data -- Extended Formulations for Online Action Selection on Big Action Sets -- Multi-Task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports -- An infrastructure and approach for infering knowledge over Big Data in the Vehicle Insurance Industry -- Unified Retrieval Model of Big Data -- Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species.
Number of the records: 1