MARC Record from marc_columbia

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001 14762336
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035 $a(OCoLC)on1128458217
035 $a(NNC)14762336
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019 $a1128886519
020 $a9781000752229$q(electronic bk.)
020 $a1000752224$q(electronic bk.)
020 $a9780429296185$q(electronic bk.)
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020 $a9781000752427$q(electronic bk. ;$qMobipocket)
020 $a1000752429$q(electronic bk. ;$qMobipocket)
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020 $a1000752623$q(electronic bk. ;$qEPUB)
020 $z9780367273286$q(hardback)
020 $z0367273284$q(hardback)
035 $a(OCoLC)1128458217$z(OCoLC)1128886519
037 $a9780429296185$bTaylor & Francis
050 4 $aQA76.95
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082 04 $a519.538$223
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049 $aZCUA
100 1 $aHima Bindu, K.,$eauthor.
245 10 $aCoefficient of variation and machine learning applications /$cK. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao
264 1 $aMilton :$bCRC Press LLC,$c[2020]
264 4 $c©2020
300 $a1 online resource (149 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aIntelligent Signal Processing and Data Analysis Ser.
520 $aCoefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models
504 $aIncludes bibliographical references and index
505 0 $aIntroduction to coefficient of variation -- CV computational strategies -- Image representation -- Supervised learning -- Applications
545 0 $aK. Himabindu is alumni of NIT- Warangal, JNTU- Hyderabad, and University of Hyderabad. She has a decade of experience in Data Mining and Machine Learning. She is currently working as a Professor in Vishnu Institute of Technology, Bhimavaram and working on a Big Data project sanctioned by Science and Research Board, Government of India. Teaching is her passion and Educational Data Mining is her current research interest. M Raghava obtained his M. Tech from Mysore University during 2003. He received his PhD from University of Hyderabad. He started his engineering teaching career in CVR College of Engineering, Hyderabad, during 2003 and successfully handled various courses related to Systems Engineering, Neural Networks, Data Engineering and Linux Internals. Currently he is serving as a Professor in CVR College of Engineering, Hyderabad. His areas of interests include Computer Vision, Regularization Theory, Sparse Representations and Graphical Models. Nilanjan Dey received his Ph. D. Degree from Jadavpur University, India, in 2015. He is an Assistant Professor in the Department of Information Technology, Techno India College of Technology, Kolkata, W.B., India. He holds an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA and Research Scientist of Laboratory of Applied Mathematical Modeling in Human Physiology, Territorial Organization of- Scientific and Engineering Unions, Bulgaria. Associate Researcher of Laboratoire RIADI, University of Manouba, Tunisia. He is the associated Member of University of Reading, London, UK and Scientific Member of - Politécnica of Porto. His research topic is Medical Imaging, Data mining, Machine learning, Computer Aided Diagnosis, Atherosclerosis etc. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence (IGI Global), US, International Journal of Rough Sets and Data Analysis (IGI Global), US, the International Journal of Synthetic Emotions (IGI Global), US, (Co-EinC) and International Journal of Natural Computing Research(IGI Global) (Co-EinC), US. Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare (AUSAH), Elsevier, Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC Press and Advances in Geospatial Technologies (AGT) Book Series, (IGI Global), US, Executive Editor of International Journal of Image Mining (IJIM), Inderscience, Associated Editor of IEEE Access and International Journal of Information Technology, Springer. He has 20 books and more than 250 research articles in peer reviewed journals and international conferences. He is the organizing committee memberof several international conferences including ITITS, W4C, ICMIR, FICTA, ICICT etc. C. Raghavendra Rao, Completed his B. Sc, M. Sc. in Statistics from Andhra University and Osmania University respectively. He obtained his Ph. D. in Statistics and M. Tech (CS & Engineering) from Osmania University. He started his carrier as a lecturer in Statistics at Osmania University in 1984. Since 1986, he has been working in the School of Mathematics and Computer/Information Sciences, University of Hyderabad. Presently he is a Professor in the School of Computer and Information Sciences, University of Hyderabad. His current research interests are Simulation & Modeling, Data Analytics, Rough Sets and Knowledge Discovery. Dr Rao is a member of the Operation Research Society of India, Indian Mathematical Society, International Association of Engineers, Society for development of statistics, Andhra Pradesh Society for Mathematical Sciences, Indian Society for Probability and Statistics, Society for High Energy Materials, International Rough Set Society, Indian Society for Rough Sets, ACM and also a Fellow of The Institution of Electronics and Telecommunication Engineers, Society for Sciences and Andhra Pradesh Academy of Science. Dr Rao Guided 13 PhDs, 55 M. Tech, 8 M. Phils. Nearly 65 Journal and 85 Proceeding Papers to his credit.
588 0 $aPrint version record
650 0 $aAnalysis of variance.
650 0 $aBig data$xStatistical methods.
650 2 $aAnalysis of Variance
650 6 $aAnalyse de variance.
650 6 $aDonnées volumineuses$xMéthodes statistiques.
650 7 $aCOMPUTERS$xMachine Theory.$2bisacsh
650 7 $aCOMPUTERS$xComputer Engineering.$2bisacsh
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aAnalysis of variance.$2fast$0(OCoLC)fst00808330
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aMorusupalli, Raghava,$eauthor.
700 1 $aDey, Nilanjan,$eauthor.
700 1 $aRao, C. Raghavendra,$eauthor.
776 08 $iPrint version:$aHima Bindu, K.$tCoefficient of variation and machine learning applications.$dBoca Raton : CRC Press, 2019$z9780367273286$w(OCoLC)1129700745
830 0 $aIntelligent signal processing and data analysis.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio14762336$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS