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000777573 0247_ $$a10.1007/978-3-319-47898-2$$2doi
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000777573 1102_ $$aInternational Neural Network Society.$$bConference on Big Data$$n(2nd :$$d2016 :$$cThessalonikē, Greece)
000777573 24510 $$aAdvances in big data :$$bproceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece /$$cPlamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco, editors.
000777573 264_1 $$aCham :$$bSpringer,$$c[2017]
000777573 264_4 $$c©2017
000777573 300__ $$a1 online resource :$$billustrations.
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000777573 4901_ $$aAdvances in intelligent systems and computing,$$x2194-5357 ;$$v529
000777573 500__ $$aInternational conference proceedings.
000777573 504__ $$aIncludes bibliographical references and index.
000777573 5050_ $$aPredicting 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.
000777573 506__ $$aAccess limited to authorized users.
000777573 520__ $$aThe 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.
000777573 588__ $$aOnline resource, title from PDF title page (viewed October 26, 2016)
000777573 650_0 $$aBig data$$vCongresses.
000777573 7001_ $$aAngelov, Plamen,$$eeditor.
000777573 7001_ $$aManolopoulos, Yannis,$$eeditor.
000777573 7001_ $$aIliadis, Lazaros,$$eeditor.
000777573 7001_ $$aRoy, Asim,$$eeditor.
000777573 7001_ $$aVellasco, Marley,$$eeditor.
000777573 77608 $$iPrint version:$$tAdvances in big data.$$dCham : Springer, [2017]$$z9783319478975$$w(OCoLC)961100816
000777573 830_0 $$aAdvances in intelligent systems and computing ;$$v529.
000777573 852__ $$bebk
000777573 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-47898-2$$zOnline Access$$91397441.1
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