Name
Abstract | ||
|---|---|---|
Least squares support vector machines (LSSVM) has been carried out in order to obtain a statistically meaningful analysis of the extended set of molecules. The combined HF with LSSVM correction approach (LSSVM/HF) has been applied to evaluate the transition energies of organic molecules. After LSSVM correction, the RMS deviations of the calculated transition energies reduce from 0.91 to 0.26 eV for HF methods. And, this LSSVM/HF is a excellent method to predict transition energies and extend the reliably and efficiently of calculated transition energies. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/FCST.2010.9 | FCST |
Keywords | Field | DocType |
rms deviations,organic molecules,chemistry computing,extended set,transition energy prediction,statistical analysis,accurate prediction,least squares support vector machines,meaningful analysis,hf methods,lssvm correction approach,rms deviation,lssvm correction,transition energies,calculated transition energy,excellent method,combined hf,hf,hf method,transition energy,support vector machines,statistically meaningful analysis,accuracy,artificial neural networks,testing,hafnium,heating | Hafnium,Least squares,Data mining,Computer science,Support vector machine,Algorithm,Real-time computing,Artificial neural network,Organic molecules,Statistical analysis | Conference |
ISBN | Citations | PageRank |
978-1-4244-7779-1 | 0 | 0.34 |
References | Authors | |
10 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ting Gao | 1 | 8 | 4.30 |
| Hui Li | 2 | 7 | 2.94 |
| Dongbing Pu | 3 | 47 | 4.84 |
| Yinghua Lu | 4 | 103 | 14.30 |
| Hai-Bin Li | 5 | 0 | 0.34 |
| Hongzhi Li | 6 | 7 | 2.22 |
| Zhong-Min Su | 7 | 8 | 5.34 |