Partitioning Clustering Based on Support Vector Ranking

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e-Distance Weighted Support Vector Regression

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets

Influencing factors and stage characteristics of intelligent construction of rural tourism in Fujian

With the help of qualitative analysis software Nvivo11, this paper makes a rooted analysis on the field text data of six rural tourism spots in Fujian Province. It shows that the influencing factors of the intelligent construction of rural tourism mainly including human capital, performance expectation, effort expectation, social impact, market demand and capital conditions. In the initial construction stage of rural tourism, the influence of capital conditions and human capital is stronger; in its development and construction stage, the impact of performance expectation is the most critical, followed by effort expectation and capital conditions; In its relatively mature stage, performance expectation and effort expectation are the dominant factors, followed by social influence. To promote the intelligent construction of rural tourism, effective measures should be taken according to the key factors in different development stages. It should be guided by the actual needs of tourists, pay attention to the fit with the positioning of rural tourism destination and target market, and avoid being intelligent for the sake of intelligence

Large Margin Distribution Machine Recursive Feature Elimination

We gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the source code of “LDM” source code and their kind technical assistance. This work is supported by the National Natural Science Foundation of China (Nos. 61472159, 61572227) and Development Project of Jilin Province of China (Nos. 20160204022GX, 2017C033). This work is also partially supported by the 2015 Scottish Crucible Award funded by the Royal Society of Edinburgh and the 2016 PECE bursary provided by the Scottish Informatics & Computer Science Alliance (SICSA).Postprin

Bis[2-(2-pyridylmethyl­eneamino)benzene­sulfonato-κ3 N,N′,O]cobalt(II) dihydrate

The title complex, [Co(C12H9N2O3S)2]·2H2O, has site symmetry 2 with the CoII cation located on a twofold rotation axis. Two tridentate 2-(2-pyridylmethyl­eneamino)benzene­sulfonate (paba) ligands chelate to the CoII cation in a distorted octa­hedral geometry. The pyridine and benzene rings in the paba ligand are oriented at a dihedral angle of 42.86 (13)°. Inter­molecular O—H⋯O and C—H⋯O hydrogen bonding is present in the crystal structure

Bis[2-(2-pyridylmethyl­eneamino)benzene­sulfonato]-κ3 N,N′,O;κ2 N,N′-copper(II)

In the mononuclear title compound, [Cu(C12H9N2O3S)2], the copper(II) salt of 2-(2-pyridylmethyl­eneamino)benzene­sulfonic acid, the CuII atom is coordinated by one O and two N atoms from a monoanion as well as by two N atoms from another monoanion in a distorted trigonal-bipyramidal environment