为解决此问题，本文提出了一种改进的支持向量机算法——DF P - PS VM算法。
To solve the problem, an improved SVM algorithm, DFP-PSVM, is presented in this paper.
将改进的支持向量回归机与B -样条网络相结合，提出了一种建立回归曲线模型的新算法。
A new algorithm for modeling regression curve is put forward in the paper, it combines B-spline network with improved support vector regression.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
本文主要致力于支持向量机、近似支持向量机的学习算法研究，特征提取的数学模型与算法的改进及其应用，聚类分析算法的收敛性证明。
This paper's main works is that: learning algorithm studies of support vector machine, mathematical model and application about feature selection, convergence analysis of clustering algorithm.
根据粗糙集理论的边界区域和V -支持向量机的优点对支持向量聚类算法进行改进。
According to the border region of rough set theory and the merits of V-support vector machine, the algorithm of support vector clustering is improved.
在隐空间中支持向量机求解过程中，引入改进粒子群算法用于搜索空间的迭代。
During the solving course of hidden space support vector machine, this paper introduces the Particle Swarm Optimize Algorithms.
并从支持向量机的几何原理出发提出了一种基于序列最小最优化算法的改进算法，验证结果证明了该改进算法的正确性。
From the geometrical theory of SVM, this thesis advanced a new method that bases on SMO. The experiment proved this method to be correct.
该文对用于回归估计的标准支持向量机(SVM)加以改进，提出了一种新的用于回归估计的支持向量机学习算法。
Based on the traditional support vector machine (SVM) for regression, a new learning algorithm of the improved SVM for regression is presented in this paper.
该文对用于回归估计的标准支持向量机(SVM)加以改进，提出了一种新的用于回归估计的支持向量机学习算法。
Based on the traditional support vector machine (SVM) for regression, a new learning algorithm of the improved SVM for regression is presented in this paper.
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