Name
Title | ||
|---|---|---|
Application of Support Vector Machine to Mobile Communications in Telephone Traffic Load of Monthly Busy Hour Prediction |
Abstract | ||
|---|---|---|
Telephone traffic of busy hour is one of indicators of load capacity of telecommunication network, which has a significant meaning to dilate and modify the network. A good performance of predicting the monthly busy hour traffic load is cared about by the mobile operators. As a promising learning theory,support vector machine (SVM) has been studied and applied in a wide area, such as financial markets and weather forecast. In this paper, we use SVM to forecast monthly busy hour traffic load of two regions in Xinjiang. A good result has been achieved via an improved grid search method for the search of hyper-parameter of SVM. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.96 | ICNC (3) |
Keywords | Field | DocType |
telephone traffic load,telephone traffic,telecommunication network,financial market,busy hour,weather forecast,support vector machine,good performance,monthly busy hour traffic,load capacity,monthly busy hour prediction,improved grid search method,good result,mobile communications,kernel,artificial neural networks,time series analysis,data mining,svm,weather forecasting,mobile communication,learning theory,mobile computing,grid computing,support vector machines,time series | Mobile computing,Hyperparameter optimization,Grid computing,Telecommunications network,Computer science,Traffic intensity,Support vector machine,Real-time computing,Artificial neural network,Mobile telephony | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Han | 1 | 20 | 3.18 |
| Zhenhong Jia | 2 | 29 | 15.13 |
| Xizhong Qin | 3 | 5 | 2.88 |
| Chun Chang | 4 | 0 | 1.35 |
| Hao Wang | 5 | 0 | 0.34 |