LEADER 00898nam0-22003011i-450- 001 990000066960403321 035 $a000006696 035 $aFED01000006696 035 $a(Aleph)000006696FED01 035 $a000006696 100 $a20011111d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aCatechismo di aritmetica da servire d'introduzione allo studio dell'aritmetica$fCarlo D'Andrea. 205 $a2. ed. riveduta ed ampliata 210 $aNapoli$cSeguin$d1845 215 $a399 p.$d23 cm 610 0 $aAritmetica 676 $a513 700 1$aD'Andrea,$bCarlo$0332286 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000066960403321 952 $a13 AR 16 D 05$b720$fFINBC 959 $aFINBC 996 $aCatechismo di aritmetica da servire d'introduzione allo studio dell'aritmetica$9111107 997 $aUNINA DB $aING01 LEADER 02737nam 2200613 a 450 001 9910437873403321 005 20200520144314.0 010 $a1-283-90917-0 010 $a1-4419-9878-0 024 7 $a10.1007/978-1-4419-9878-1 035 $a(CKB)2670000000308607 035 $a(EBL)1081693 035 $a(OCoLC)819571506 035 $a(SSID)ssj0000811768 035 $a(PQKBManifestationID)11458917 035 $a(PQKBTitleCode)TC0000811768 035 $a(PQKBWorkID)10850599 035 $a(PQKB)10498465 035 $a(DE-He213)978-1-4419-9878-1 035 $a(MiAaPQ)EBC1081693 035 $a(PPN)168291568 035 $a(EXLCZ)992670000000308607 100 $a20121018d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobust data mining /$fPetros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (66 p.) 225 0$aSpringerBriefs in optimization,$x2190-8354 300 $aDescription based upon print version of record. 311 $a1-4419-9877-2 320 $aIncludes bibliographical references. 327 $a1. Introduction -- 2. Least Squares Problems -- 3. Principal Component Analysis -- 4. Linear Discriminant Analysis -- 5. Support Vector Machines -- 6. Conclusion. 330 $aData uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents  the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field. 410 0$aSpringerBriefs in Optimization,$x2190-8354 606 $aData mining 606 $aRobust optimization 615 0$aData mining. 615 0$aRobust optimization. 676 $a006.312 700 $aXanthopoulos$b Petros$01064749 701 $aPardalos$b P. M$g(Panos M.),$f1954-$0318341 701 $aTrafalis$b Theodore B$01751789 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437873403321 996 $aRobust data mining$94186904 997 $aUNINA