Advances in Big Data (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
348
Utgivningsdatum
2016-10-09
Upplaga
1st ed. 2017
Förlag
Springer International Publishing AG
Medarbetare
Angelov, Plamen (ed.), Manolopoulos, Yannis (ed.), Iliadis, Lazaros (ed.), Roy, Asim (ed.), Vellasco, Marley (ed.)
Illustratör/Fotograf
Bibliographie 101 schwarz-weiße Abbildungen
Illustrationer
101 Illustrations, black and white; XVII, 348 p. 101 illus.
Dimensioner
234 x 156 x 19 mm
Vikt
513 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783319478975

Advances in Big Data

Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece

av Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco
Häftad,  Engelska, 2016-10-09
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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 2325, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.
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Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco

Innehållsförteckning

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.

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