LEADER 01097nam--2200385---450- 001 990003196280203316 005 20090213101017.0 010 $a1-58829-562-1 035 $a000319628 035 $aUSA01000319628 035 $a(ALEPH)000319628USA01 035 $a000319628 100 $a20090213d2007----km-y0itay50------ba 101 $aeng 102 $aUS 105 $a||||||||001yy 200 1 $aQuantum dots$eapplications in biology$fedited by Marcel P. Bruchez, Charles Z. Hotz 210 $aTotowa$cHumana Press$d2007 215 $aXII, 257 p.$d24 cm 225 2 $aMethods in molecular biology$v374 410 0$12001$aMethods in molecular biology$v374 454 1$12001 461 1$1001-------$12001 606 0 $aBiochimica$2BNCF 676 $a572.36 702 1$aBRUCHEZ,$bMarcel P. 702 1$aHOTZ,$bCharles Z. 801 0$aIT$bsalbc$gISBD 912 $a990003196280203316 951 $a572.36 QUA$b5038 Farm.$c572.36$d00186509 959 $aBK 969 $aFAR 979 $aFIORELLA$b90$c20090213$lUSA01$h1010 996 $aQuantum dots$9757277 997 $aUNISA LEADER 02916oam 2200721I 450 001 9910799907903321 005 20200520144314.0 010 $a0-429-10650-5 010 $a1-4398-3928-X 024 7 $a10.1201/b11423 035 $a(CKB)2670000000175763 035 $a(EBL)830224 035 $a(SSID)ssj0001139458 035 $a(PQKBManifestationID)11651054 035 $a(PQKBTitleCode)TC0001139458 035 $a(PQKBWorkID)11213598 035 $a(PQKB)10959379 035 $a(SSID)ssj0000580990 035 $a(PQKBManifestationID)12234789 035 $a(PQKBTitleCode)TC0000580990 035 $a(PQKBWorkID)10526328 035 $a(PQKB)11497283 035 $a(Au-PeEL)EBL830224 035 $a(CaPaEBR)ebr10546312 035 $a(CaONFJC)MIL692885 035 $a(OCoLC)899154945 035 $a(OCoLC)785416992 035 $a(CaSebORM)9781439839287 035 $a(MiAaPQ)EBC830224 035 $a(EXLCZ)992670000000175763 100 $a20180331d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCost-sensitive machine learning /$fedited by Balaji Krishnapuram, Shipeng Yu, Bharat Rao 205 $a1st edition 210 1$aBoca Raton, Fla. :$cCRC Press,$d2012. 215 $a1 online resource (316 p.) 225 1 $aChapman & Hall/CRC machine learning & pattern recognition series 300 $a"A Chapman & Hall book." 311 $a1-4665-4817-7 311 $a1-322-61603-5 311 $a1-4398-3925-5 320 $aIncludes bibliographical references. 327 $apt. 1. Theoretical underpinnings of cost-sensitive machine learning -- pt. 2. Cost-sensitive machine learning applications. 330 $aIn machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost o 410 0$aChapman & Hall/CRC machine learning & pattern recognition series. 517 3 $aCost sensitive machine learning 606 $aMachine learning$xCost effectiveness 615 0$aMachine learning$xCost effectiveness. 676 $a006.31 701 $aKrishnapuram$b Balaji$01587700 701 $aYu$b Shipeng$0920799 701 $aRao$b Bharat$0868293 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910799907903321 996 $aCost-sensitive machine learning$93875960 997 $aUNINA