Search results for "meter"

showing 10 items of 16915 documents

CCDC 641246: Experimental Crystal Structure Determination

2008

Related Article: B.Abarca, R.Ballesteros, M.Chadlaoui, C.R.de Arellano, J.A.Real|2007|Eur.J.Inorg.Chem.||4574|doi:10.1002/ejic.200700640

tetrakis(mu~3~-(Pyridin-2-yl)-(6-([123]triazolo[15-a]pyridin-3-yl)pyridin-2-yl)(methoxy)methanone)-tetra-copper(ii) tetranitrate octahydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 691439: Experimental Crystal Structure Determination

2008

Related Article: A.Alberola, R.Llusar, C.Vicent, J.Andres, V.Polo, C.J.Gomez-Garcia|2008|Inorg.Chem.|47|3661|doi:10.1021/ic7022083

tetrakis(mu~3~-Seleno)-carbonyl-trichloro-tris(12-bis(dimethylphosphino)ethane)-cobalt-tri-molybdenum dichloromethane solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 691440: Experimental Crystal Structure Determination

2008

Related Article: A.Alberola, R.Llusar, C.Vicent, J.Andres, V.Polo, C.J.Gomez-Garcia|2008|Inorg.Chem.|47|3661|doi:10.1021/ic7022083

tetrakis(mu~3~-Seleno)-tetrachloro-tris(12-bis(dimethylphosphino)ethane)-cobalt-tri-molybdenum dichloromethane solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 691441: Experimental Crystal Structure Determination

2008

Related Article: A.Alberola, R.Llusar, C.Vicent, J.Andres, V.Polo, C.J.Gomez-Garcia|2008|Inorg.Chem.|47|3661|doi:10.1021/ic7022083

tetrakis(mu~3~-Seleno)-tetrachloro-tris(12-bis(dimethylphosphino)ethane)-cobalt-tri-molybdenum tetracyanoquinodimethane dichloromethane solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 1020496: Experimental Crystal Structure Determination

2014

Related Article: Huayan Yang , Yu Wang , Juanzhu Yan , Xi Chen , Xin Zhang , Hannu Häkkinen , and Nanfeng Zheng|2014|J.Am.Chem.Soc.|136|7197|doi:10.1021/ja501811j

tetrakis(tetraphenylphosphonium) icosakis(mu-4-(trifluoromethyl)benzenethiolato)-trideca-gold-dodeca-copper dichloromethane solvate tetrahydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 1979322: Experimental Crystal Structure Determination

2020

Related Article: Noelia Maldonado, Josefina Perles, José Ignacio Martínez, Carlos J. Gómez-García, María-Luisa Marcos, Pilar Amo-Ochoa|2020|Cryst.Growth Des.|20|5097|doi:10.1021/acs.cgd.0c00268

tetrakis{mu-[3-(24-dioxo-34-dihydropyrimidin-1(2H)-yl)propanoato]}-aqua-methanol-di-copper(ii) dihydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 659301: Experimental Crystal Structure Determination

2008

Related Article: E.J.Juarez-Perez, C.Vinas, A.Gonzalez-Campo, F.Teixidor, R.Sillanpaa, R.Kivekas, R.Nunez|2008|Chem.-Eur.J.|14|4924|doi:10.1002/chem.200702013

tetramethylammonium 1-(dimethylsilyl)-33'-commo-bis(3-cobalta-12-dicarba-closo-dodecaborate)Space GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 255485: Experimental Crystal Structure Determination

2005

Related Article: D.Armentano, G.De Munno, F.Lloret, M.Julve|2005|CrystEngComm|7|57|doi:10.1039/b417251e

tetraphenylarsonium bis(oxalato)-(110-phenanthroline)-iron(iii) monohydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 173965: Experimental Crystal Structure Determination

2003

Related Article: G.Marinescu, R.Lecouezec, D.Armentano, G.De Munno, M.Andruh, S.Uriel, R.Llusar, F.Lloret, M.Julve|2002|Inorg.Chim.Acta|336|46|doi:10.1016/S0020-1693(02)00880-0

tetraphenylphosphonium (22'-bipyrimidine)-bis(oxalato)-chromium(iii) monohydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression

2020

Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM. peerReviewed

the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM)koneoppiminenemphasizing the utility of the problem transformation especially with the EMLM.Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methodsin problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential
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