LEADER 01283nam a22003255i 4500 001 991002214369707536 007 cr nn 008mamaa 008 121227s1985 gw | s |||| 0|eng d 020 $a9783540397496 035 $ab14139091-39ule_inst 040 $aBibl. Dip.le Aggr. Matematica e Fisica - Sez. Matematica$beng 082 04$a518$223 084 $aAMS 39-XX 084 $aAMS 58F 245 00$aIteration theory and its functional equations$h[e-book] :$bproceedings of the international symposium held at Schloss Hofen (Lochau), Austria, Sept. 28-Oct. 1, 1984 /$cedited by Roman Liedl, Ludwig Reich, Gyorgy Targonski 260 $aBerlin :$bSpringer,$c1985 300 $a1 online resource (x, 234 p.) 440 0$aLecture Notes in Mathematics,$x0075-8434 ;$v1163 650 0$aMathematics 650 0$aNumerical analysis 700 1 $aLiedl, Roman 700 1 $aReich, Ludwig 700 1 $aTargonski, Gyorgy 773 0 $aSpringer eBooks 856 40$uhttp://dx.doi.org/10.1007/BFb0076410$zAn electronic book accessible through the World Wide Web 907 $a.b14139091$b03-03-22$c05-09-13 912 $a991002214369707536 996 $aIteration theory and its functional equations$979886 997 $aUNISALENTO 998 $ale013$b05-09-13$cm$d@ $e-$feng$ggw $h0$i0 LEADER 03991nam 2200613 450 001 9910790503303321 005 20220223214314.0 010 $a1-68015-358-7 010 $a1-78216-215-1 035 $a(CKB)2550000001138614 035 $a(OCoLC)862380117 035 $a(CaPaEBR)ebrary10794279 035 $a(SSID)ssj0001139763 035 $a(PQKBManifestationID)11649255 035 $a(PQKBTitleCode)TC0001139763 035 $a(PQKBWorkID)11220486 035 $a(PQKB)11737178 035 $a(Au-PeEL)EBL1343653 035 $a(CaPaEBR)ebr10794279 035 $a(CaONFJC)MIL538284 035 $a(CaSebORM)9781782162148 035 $a(MiAaPQ)EBC1343653 035 $a(PPN)227990579 035 $a(EXLCZ)992550000001138614 100 $a20111102d2013 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine learning with R /$fBrett Lantz 205 $a1st edition 210 1$aBirmingham :$cPackt Publishing,$d2013. 215 $a1 online resource (396 p.) 225 1 $aCommunity experience distilled 300 $aIncludes index. 311 $a1-78216-214-3 311 $a1-306-07033-3 330 $aR gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ? this book will take you methodically through every stage of applying machine learning. Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R?a cross-platform, zero-cost statistical programming environment?there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data. 410 0$aCommunity experience distilled. 606 $aMachine learning$xStatistical methods$vHandbooks, manuals, etc 606 $aR (Computer program language)$vHandbooks, manuals, etc 606 $aProgramming languages (Electronic computers) 615 0$aMachine learning$xStatistical methods 615 0$aR (Computer program language) 615 0$aProgramming languages (Electronic computers) 700 $aLantz$b Brett$01466983 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790503303321 996 $aMachine learning with R$93677617 997 $aUNINA