LEADER 01335nam a22003011i 4500 001 991002989809707536 005 20040511163114.0 008 040624s1987 it a||||||||||||||||ita 035 $ab13007865-39ule_inst 035 $aARCHE-096554$9ExL 040 $aDip.to Beni Culturali$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a930.1074 245 03$aLa collezione Borgia /$ca cura di M.T. Falconi Amorelli ; con la collaborazione di G. Fabrini e O. Colazingari 260 $a[Roma] :$bBorgia ;$aL'Erma di Bretschneider,$cc1987 300 $aVII, 44 p., LV p. di tav. :$bill. ;$c32 cm 440 0$aCollana di studi archeologici ;$v2 650 4$aArte$xAntichità$xCollezioni 650 4$aCollezioni private$zRoma$xBorgia (Famiglia)$vCataloghi 700 1 $aFalconi Amorelli, Maria Teresa 700 1 $aFabrini, Giovanna M. 700 1 $aColazingari, Olga 907 $a.b13007865$b02-04-14$c12-07-04 912 $a991002989809707536 945 $aLE001 AR V 123 4$g1$i2001000112746$lle001$nC. 1$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i13618647$z12-07-04 945 $aLE016 AR 1 32 $g1$i2016000042443$lle016$nFondo Nenci$on$pE20.00$q-$rn$so $t0$u0$v0$w0$x0$y.i14375746$z22-02-07 996 $aCollezione Borgia$9282310 997 $aUNISALENTO 998 $ale001$ale016$b12-07-04$cm$da $e-$fita$git $h3$i1 LEADER 04342nam 22006855 450 001 9910512174803321 005 20251121144906.0 010 $a3-030-91445-3 024 7 $a10.1007/978-3-030-91445-5 035 $a(CKB)5100000000152461 035 $a(MiAaPQ)EBC6858132 035 $a(Au-PeEL)EBL6858132 035 $a(PPN)259384879 035 $a(OCoLC)1287996875 035 $a(DE-He213)978-3-030-91445-5 035 $a(EXLCZ)995100000000152461 100 $a20211129d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Analytics and Learning on Temporal Data $e6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers /$fedited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (202 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13114 311 08$a3-030-91444-5 327 $aOral Presentation -- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification -- State Space approximation of Gaussian Processes for time-series forecasting -- Fast Channel Selection for Scalable Multivariate Time Series Classification -- Temporal phenotyping for characterisation of hospital care pathways of COVID patients -- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation -- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control -- Detection of critical events in renewable energy production time series -- Poster Presentation -- Multimodal Meta-Learning for Time Series Regression -- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series -- State discovery and prediction from multivariate sensor data -- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds -- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information. 330 $aThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. . 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13114 606 $aArtificial intelligence 606 $aData mining 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aEducation$xData processing 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 606 $aComputer Communication Networks 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputers and Education 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 0$aEducation$xData processing. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer Communication Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputers and Education. 676 $a006.31 702 $aLemaire$b Vincent 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910512174803321 996 $aAdvanced Analytics and Learning on Temporal Data$93083342 997 $aUNINA