LEADER 03300nam 2200517 450 001 996517753703316 005 20230729144157.0 010 $a3-031-26419-3 024 7 $a10.1007/978-3-031-26419-1 035 $a(MiAaPQ)EBC7216769 035 $a(Au-PeEL)EBL7216769 035 $a(CKB)26271466700041 035 $a(DE-He213)978-3-031-26419-1 035 $a(PPN)269092587 035 $a(EXLCZ)9926271466700041 100 $a20230729d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning and knowledge discovery in databases $eEuropean conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, proceedings, Part V /$fMassih-Reza Amini [and five others] 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer Nature Switzerland AG,$d[2023] 210 4$dİ2023 215 $a1 online resource (669 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13717 311 08$aPrint version: Amini, Massih-Reza Machine Learning and Knowledge Discovery in Databases Cham : Springer International Publishing AG,c2023 9783031264184 327 $aSupervised learning -- Probabilistic inference -- Optimal transport -- Optimization -- Quantum, hardware -- Sustainability. 330 $aThe multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13717 606 $aData mining$vCongresses 606 $aDatabases$vCongresses 606 $aMachine learning$vCongresses 615 0$aData mining 615 0$aDatabases 615 0$aMachine learning 676 $a006.31 700 $aAmini$b Massih-Reza$01060956 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996517753703316 996 $aMachine learning and knowledge discovery in databases$93406175 997 $aUNISA LEADER 01622nam 2200373 n 450 001 9911007053003321 005 20230315084917.0 010 $a1-62870-994-4 035 $a(CKB)3170000000070978 035 $a(NjHacI)993170000000070978 035 $a(EXLCZ)993170000000070978 100 $a20230315d2001 uu 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSurface Reflectance and Colour $eIts Specification and Measurement for Designers /$fDavid Loe 210 1$aLondon :$cThe Society of Light and Lighting : National Physical Laboratory,$d2001. 215 $a1 online resource (32 pages) 225 1 $aLighting Guide ;$v11 311 $a1-903287-14-6 330 $aAlmost all calculations used in the design of interior lighting involve detailed knowledge of the reflectance values of the walls and other surfaces which are rarely known. All too often the unfortunate designer has to work on an intelligent guess. This lighting guide attempts to tackle this problem in a way which is useful to lighting designers. Its chapters take the reader through the basic physics of light reflection, the reflection properties of building materials and some relevant systems of colour specification. 410 0$aLighting Guide$v11. 606 $aColorimetry 615 0$aColorimetry. 676 $a535.60287 700 $aLoe$b David$01823768 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9911007053003321 996 $aSurface Reflectance and Colour$94390704 997 $aUNINA