001434652 000__ 05336cam\a2200757\i\4500 001434652 001__ 1434652 001434652 003__ OCoLC 001434652 005__ 20230309003811.0 001434652 006__ m\\\\\o\\d\\\\\\\\ 001434652 007__ cr\un\nnnunnun 001434652 008__ 210224s2021\\\\sz\a\\\\o\\\\\101\0\eng\d 001434652 019__ $$a1249943763 001434652 020__ $$a9783030676643$$q(electronic bk.) 001434652 020__ $$a3030676641$$q(electronic bk.) 001434652 020__ $$z9783030676636 001434652 0247_ $$a10.1007/978-3-030-67664-3$$2doi 001434652 035__ $$aSP(OCoLC)1241066181 001434652 040__ $$aDKU$$beng$$erda$$epn$$cDKU$$dOCLCO$$dOCLCQ$$dYDXIT$$dGW5XE$$dOCLCO$$dEBLCP$$dOCLCF$$dLEATE$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ 001434652 049__ $$aISEA 001434652 050_4 $$aQ325.5 001434652 08204 $$a006.3/1$$223 001434652 1112_ $$aECML PKDD (Conference)$$d(2020 :$$cOnline) 001434652 24510 $$aMachine learning and knowledge discovery in databases :$$bEuropean conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.$$nPart III /$$cFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera (eds.). 001434652 24630 $$aECML PKDD 2020 001434652 264_1 $$aCham :$$bSpringer,$$c[2021] 001434652 300__ $$a1 online resource (xliii, 755 pages) :$$billustrations (chiefly color) 001434652 336__ $$atext$$btxt$$2rdacontent 001434652 337__ $$acomputer$$bc$$2rdamedia 001434652 338__ $$aonline resource$$bcr$$2rdacarrier 001434652 4901_ $$aLecture notes in computer science. Lecture notes in artificial intelligence ;$$v12459 001434652 500__ $$aInternational conference proceedings. 001434652 500__ $$aIncludes author index. 001434652 5050_ $$aCombinatorial optimization -- large-scale optimization and differential privacy -- boosting and ensemble methods -- Bayesian methods -- architecture of neural networks -- graph neural networks -- Gaussian processes -- computer vision and image processing -- natural language processing.-bioinformatics. 001434652 506__ $$aAccess limited to authorized users. 001434652 520__ $$aThe 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio- ) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. 001434652 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 23, 2021). 001434652 650_0 $$aMachine learning$$vCongresses. 001434652 650_0 $$aData mining$$vCongresses. 001434652 650_0 $$aData structures (Computer science) 001434652 650_0 $$aArtificial intelligence. 001434652 650_0 $$aNumerical analysis. 001434652 650_0 $$aApplication software. 001434652 650_6 $$aApprentissage automatique$$vCongrès. 001434652 650_6 $$aExploration de données (Informatique)$$vCongrès. 001434652 650_6 $$aStructures de données (Informatique) 001434652 650_6 $$aIntelligence artificielle. 001434652 650_6 $$aAnalyse numérique. 001434652 650_6 $$aLogiciels d'application. 001434652 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772 001434652 655_7 $$aConference papers and proceedings.$$2lcgft 001434652 655_7 $$aActes de congrès.$$2rvmgf 001434652 655_0 $$aElectronic books. 001434652 7001_ $$aHutter, Frank,$$eeditor. 001434652 7001_ $$aKersting, Kristian,$$eeditor. 001434652 7001_ $$aLijffijt, Jefrey,$$eeditor. 001434652 7001_ $$aValera, Isabel,$$eeditor. 001434652 77608 $$iPrint version: $$z9783030676636 001434652 77608 $$iPrint version: $$z9783030676650 001434652 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence. 001434652 830_0 $$aLecture notes in computer science ;$$v12459. 001434652 852__ $$bebk 001434652 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67664-3$$zOnline Access$$91397441.1 001434652 909CO $$ooai:library.usi.edu:1434652$$pGLOBAL_SET 001434652 980__ $$aBIB 001434652 980__ $$aEBOOK 001434652 982__ $$aEbook 001434652 983__ $$aOnline 001434652 994__ $$a92$$bISE