Machine Learning for Big Data Analysis / ed. by Siddhartha Bhattacharyya, Hrishikesh Bhaumik, Anirban Mukherjee, Sourav De.
Contributor(s): Adate, Amit [contributor.]
| Bhattacharyya, Siddhartha [contributor.] | Bhattacharyya, Siddhartha [editor.] | Bhaumik, Hrishikesh [contributor.] | Bhaumik, Hrishikesh [editor.] | Bick, Markus [contributor.] | Blesik, Till [contributor.] | Chakraborty, Susanta [contributor.] | Choudhury, Abantika [contributor.] | De, Sourav [editor.] | Deb, Moumita [contributor.] | Gorbachev, S. V [contributor.] | Henesey, Lawrence [contributor.] | Ho, Chiung Ching [contributor.] | Mishra, Deepak [contributor.] | Mukherjee, Anirban [editor.] | Murawski, Matthias [contributor.] | Nirala, Satish [contributor.] | Ponraj, D. Narain [contributor.] | Sagayam, K. Martin [contributor.] | Sur, Surangam [contributor.] | Tripathy, B. K [contributor.] | Vasanth, X. Ajay [contributor.] | Vurucu, Murat [contributor.].
Material type: 
BookSeries: De Gruyter Frontiers in Computational Intelligence , 1.Publisher: Berlin ; Boston : De Gruyter, [2018]Copyright date: ©2019Description: 1 online resource (X, 183 p.).Content type: text Media type: computer Carrier type: online resourceISBN: 9783110551433.Subject(s): Big data | Machine learning | Quantitative research | Artificial Intelligence | Machine Learning | Signal Processing | COMPUTERS / Intelligence (AI) & SemanticsAdditional physical formats: No title; No titleDDC classification: 005.7 Online resources: Click here to access online | Click here to access online | Cover Issued also in print.Frontmatter -- Preface -- Contents -- 1. Applying big data analytics to psychometric micro-targeting -- 2. Keyframe selection for video indexing using an approximate minimal spanning tree -- 3. Deep learning techniques for image processing -- 4. Connecting cities using smart transportation: an overview -- 5. Model of intellectual analysis of multidimensional semi-structured data based on deep neuro-fuzzy networks -- 6. Image fusion in remote sensing based on sparse sampling method and PCNN techniques -- Index
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This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent research.
Issued also in print.
Mode of access: Internet via World Wide Web.
In English.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)
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