LEADER 03540nam 22007935 450 001 9910484122003321 005 20250624084713.0 010 $a3-319-12054-9 024 7 $a10.1007/978-3-319-12054-6 035 $a(CKB)3710000000269682 035 $a(SSID)ssj0001372702 035 $a(PQKBManifestationID)11888494 035 $a(PQKBTitleCode)TC0001372702 035 $a(PQKBWorkID)11305040 035 $a(PQKB)10191555 035 $a(DE-He213)978-3-319-12054-6 035 $a(MiAaPQ)EBC6282950 035 $a(MiAaPQ)EBC5590646 035 $a(Au-PeEL)EBL5590646 035 $a(OCoLC)895035420 035 $a(PPN)182097714 035 $a(EXLCZ)993710000000269682 100 $a20141023d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aModeling Decisions for Artificial Intelligence $e11th International Conference, MDAI 2014, Tokyo, Japan, October 29-31, 2014, Proceedings /$fedited by Vicenç Torra, Yasuo Narukawa, Yasunori Endo 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XVIII, 241 p. 47 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v8825 300 $aIncludes index. 311 08$a3-319-12053-0 327 $aAggregation Operators and Decision Making -- Inference Systems Optimization -- Clustering and Similarity -- Data Mining and Data Privacy. 330 $aThis book constitutes the proceedings of the 11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014, held in Tokyo, Japan, in October 2014. The 19 revised full papers presented together with an invited paper were carefully reviewed and selected from 38 submissions. They deal with the theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques and are organized in topical sections on aggregation operators and decision making, optimization, clustering and similarity, and data mining and data privacy. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v8825 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aData mining 606 $aInformation storage and retrieval systems 606 $aNumerical analysis 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 606 $aData Mining and Knowledge Discovery 606 $aInformation Storage and Retrieval 606 $aNumerical Analysis 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 0$aData mining. 615 0$aInformation storage and retrieval systems. 615 0$aNumerical analysis. 615 14$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aNumerical Analysis. 676 $a006.3 702 $aTorra$b Vicenç$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNarukawa$b Yasuo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEndo$b Yasunori$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484122003321 996 $aModeling Decisions for Artificial Intelligence$9772296 997 $aUNINA LEADER 03114nam 2200721 a 450 001 9911019871903321 005 20200520144314.0 010 $a9786612348211 010 $a9781282348219 010 $a1282348213 010 $a9781119952176 010 $a1119952174 010 $a9780470773208 010 $a0470773200 010 $a9780470517239 010 $a0470517239 035 $a(CKB)1000000000687944 035 $a(SSID)ssj0000289305 035 $a(PQKBManifestationID)11205544 035 $a(PQKBTitleCode)TC0000289305 035 $a(PQKBWorkID)10401539 035 $a(PQKB)11416543 035 $a(MiAaPQ)EBC470102 035 $a(OCoLC)264615341 035 $a(Perlego)2751803 035 $a(EXLCZ)991000000000687944 100 $a20080115d2008 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAssessing risk in sex offenders $ea practitioner's guide /$fLeam A. Craig, Kevin D. Browne and Anthony R. Beech 210 $aChichester, England ;$aHoboken, NJ $cWiley$dc2008 215 $axvii, 249 p. $cill 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9780470018989 311 08$a0470018984 311 08$a9780470018972 311 08$a0470018976 320 $aIncludes bibliographical references (p. [201]-230) and index. 330 8 $aAssessing Risk in Sex Offenders: A Practitioner's Guide is a handy resource for forensic practitioners responsible for assessing an managing sexual offenders at risk of recidivism. It covers the risk factors associated with sexual recidivism, evaluates risk assessment approaches and offers guidance on how to conduct forensic evaluations. Written by an expert author team, Assessing risk in Sex Offenders: A Practitioner's Guide examines: * The characteristics of sexual offenders * Methodological considerations in measuring predictive accuracy * Static and dynamic factors * Structured risk assessments * Treatment of sexual offenders * Policy and practices Assessing Risk in Sex Offenders: A Practitioner's Guide is an essential resource for clinical and forensic psychologists, forensic psychiatrists, undergraduate and postgraduate students in forensic and clinical psychology, and prison and probation officers. 606 $aSex offenders$zUnited States 606 $aCriminal behavior, Prediction of$zUnited States 606 $aRisk assessment$zUnited States 606 $aRecidivism$zUnited States$xPrevention 606 $aEvidence, Expert$zUnited States 615 0$aSex offenders 615 0$aCriminal behavior, Prediction of 615 0$aRisk assessment 615 0$aRecidivism$xPrevention. 615 0$aEvidence, Expert 676 $a364.4/1 700 $aCraig$b Leam$0882826 701 $aBrowne$b Kevin D$g(Kevin Dominic)$01838517 701 $aBeech$b Anthony R$01838516 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019871903321 996 $aAssessing risk in sex offenders$94420655 997 $aUNINA