LEADER 04707nam 22006015 450 001 9910254940303321 005 20200701235103.0 010 $a1-4899-7641-8 024 7 $a10.1007/978-1-4899-7641-3 035 $a(CKB)3710000000493529 035 $a(EBL)4067980 035 $a(DE-He213)978-1-4899-7641-3 035 $a(MiAaPQ)EBC4067980 035 $a(PPN)224302663 035 $a(EXLCZ)993710000000493529 100 $a20151030d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Models and Algorithms for Big Data Classification $eThinking with Examples for Effective Learning /$fby Shan Suthaharan 205 $a1st ed. 2016. 210 1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d2016. 215 $a1 online resource 225 1 $aIntegrated Series in Information Systems,$x1571-0270 ;$v36 300 $aDescription based upon print version of record. 311 $a1-4899-7640-X 320 $aIncludes bibliographical references and index. 327 $aScience of Information -- Part I Understanding Big Data -- Big Data Essentials -- Big Data Analytics -- Part II Understanding Big Data Systems -- Distributed File System -- MapReduce Programming Platform -- Part III Understanding Machine Learning -- Modeling and Algorithms -- Supervised Learning Models -- Supervised Learning Algorithms -- Support Vector Machine -- Decision Tree Learning -- Part IV Understanding Scaling-Up Machine Learning -- Random Forest Learning -- Deep Learning Models -- Chandelier Decision Tree -- Dimensionality Reduction. 330 $aThis book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems. 410 0$aIntegrated Series in Information Systems,$x1571-0270 ;$v36 606 $aManagement 606 $aDatabase management 606 $aArtificial intelligence 606 $aManagement$3https://scigraph.springernature.com/ontologies/product-market-codes/515000 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aManagement. 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 14$aManagement. 615 24$aDatabase Management. 615 24$aArtificial Intelligence. 676 $a658.4033 700 $aSuthaharan$b Shan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0922406 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254940303321 996 $aMachine Learning Models and Algorithms for Big Data Classification$92069866 997 $aUNINA