| 改进的基于支持向量机模型剪接位点的预测 |
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| 引用本文： | 王丽美,;郑大军,;郑程友.改进的基于支持向量机模型剪接位点的预测[J].宜宾学院学报,2014(12):93-98. |
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| 作者姓名： | 王丽美 ;郑大军 ;郑程友 |
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| 作者单位： | [1]临沧师范高等专科学校数理系,云南临沧677000; [2]福建农林大学计算机与信息学院,福建福州350002; [3]桂林理工大学机械与控制工程学院,广西桂林541000 |
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| 摘 要： | 在采用概率编码的基础上,利用支持向量机模型对人类剪接位点进行预测,重点研究了基于核主成分分析方法对最终预测模型的影响.从实验结果看,这种改进的基于支持向量机模型剪接位点的预测在敏感性和特异性上优于其他模型.
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| 关 键 词： | 剪接 预测 特征提取 支持向量机 核主成分分析 |
Prediction on Slice Site Based on Improved SVM Model |
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| Institution: | WANG Limei, ZHENG Dajun, ZHENG Chengyou (1.Department of Mathematics and Physics, Lincang Teachers＇ College, Lincang, Yunnan 677000, China; 2. College of Computer and Information Technology, Fufian Agriculture and Forestry University, Fuzhou, Fujian 350002, China; 3.College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541000, China) |
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| Abstract: | By adopting probability encoding, the support vector machine（SVM） model was used to make a prediction onhuman＇s slice site and the impact of KPCA method on the final prediction model was discussed. The experiment showsthat this improved SVM model is superior to other models in sensitivity and specificity on the prediction of slice site. |
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| Keywords: | slice prediction feature extraction SVM KPC |
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