Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vect...Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a text classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real Chinese corpus from FudanUniversityis used to demonstrate the good performance of the SVM- MK.展开更多
为了提高果蝇优化算法的种群多样性和果蝇搜索的遍历性,有效提高算法的收敛精度,提出一种改进的果蝇算法(Improving fruit fly optimization algorithm,IFOA),仿真实验表明,IFOA算法保持了搜索过程中的搜索尺度变化,平衡了算法的全局与...为了提高果蝇优化算法的种群多样性和果蝇搜索的遍历性,有效提高算法的收敛精度,提出一种改进的果蝇算法(Improving fruit fly optimization algorithm,IFOA),仿真实验表明,IFOA算法保持了搜索过程中的搜索尺度变化,平衡了算法的全局与局部搜索能力.在此基础上,为了改善支持向量机模型参数选择的随机性和盲目性,提高模式分类的准确率,提出并建立了一种IFOA-SVM模式分类模型.该方法将IFOA算法引入到支持向量机模型参数优化中,建立性能最优的支持向量机模型.应用该模型对UCI机器学习数据库中wine数据集进行模式分类研究,通过算法对比分析,结果表明:提出的改进果蝇优化算法在收敛速度和寻优效率上均有一定的提高,依此而建立的IFOA-SVM模式分类模型具有较准确的分类准确率,从而也验证了该模式分类方法在wine数据集分类应用中的有效性.展开更多
文摘Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a text classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real Chinese corpus from FudanUniversityis used to demonstrate the good performance of the SVM- MK.
文摘为了提高果蝇优化算法的种群多样性和果蝇搜索的遍历性,有效提高算法的收敛精度,提出一种改进的果蝇算法(Improving fruit fly optimization algorithm,IFOA),仿真实验表明,IFOA算法保持了搜索过程中的搜索尺度变化,平衡了算法的全局与局部搜索能力.在此基础上,为了改善支持向量机模型参数选择的随机性和盲目性,提高模式分类的准确率,提出并建立了一种IFOA-SVM模式分类模型.该方法将IFOA算法引入到支持向量机模型参数优化中,建立性能最优的支持向量机模型.应用该模型对UCI机器学习数据库中wine数据集进行模式分类研究,通过算法对比分析,结果表明:提出的改进果蝇优化算法在收敛速度和寻优效率上均有一定的提高,依此而建立的IFOA-SVM模式分类模型具有较准确的分类准确率,从而也验证了该模式分类方法在wine数据集分类应用中的有效性.
