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
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
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
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
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
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
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
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
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
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