Research on the fuzzy SVMs based on fuzzy coefficient programming.
研究基于模糊系数规划的模糊支持向量分类机。
In linear programming linear SVMs, the bound of the VC dimension is loosened properly.
在线性规划支撑矢量机中，对其VC维数界作了适当的放宽。
An incremental learning algorithm using multiple support vector machines (SVMs) is proposed.
给出了使用多支持向量机进行增量学习的算法。
Since the analysis is from two viewpoints, it can cover and distinguish most of the existing SVMs.
由于分析从两个角度进行，所提出的方法能够涵盖，并区分绝大多数现有SVM。
Experimental results indicate that it is feasible to adopt SVMs for stressed speech classification.
实验结果表明，采用支持向量机方法进行变异语音分类是可行的。
Based on the theory and technique of the support vector machines (SVMs), an assessment system was built.
基于支持向量机的理论和技术，构建了换档质量评价系统。
The L2 norm soft margin algorithms in SVMs can change each linearly inseparable problem into a separable one.
支持向量机中的L2范数软边缘算法可以将线性不可分问题转化为线性可分问题。
Firstly, SOM is applied to cluster for the remote sensing target, then SVMs are used to classify each cluster.
该方法首先利用SOM对目标进行聚类，然后应用SVM方法对其进行分类识别。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
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。
For RS object detection based on SVMs, the false alarm rate is commonly high because of limited object samples and relative complex background.
在基于支持向量机的遥感影像目标检测中，因为有限的目标样本和相对复杂的背景，造成检测结果的虚警率偏高。
Kernal methods and support Vector Machines (SVMs) are related to Gaussian processes and can also be used in classification and regression problems.
内核方法和支持向量机(SVMs)与高斯过程相关并应用于分类和回归问题。
Studying from the statistical theory, based on the general principle of SVMs, this paper analyzes and compares the capability of the different kinds of SVMs.
文章从统计学习理论入手，在讲述SVM一般原理的基础上，分析比较不同种的支持向量机的性能。
The method can reduce the dimensions of the data set and the complexity of the model of SVMs, and doesn't affect its classification and prediction performance.
该方法可降低数据空间维数和支持向量机处理过程的复杂度，但不会降低分类和预测性能。
Moreover, SVMs can change a nonlinear learning problem in to a linear learning problem in order to reduce the algorithm complexity by using the kernel function idea.
又由于采用了核函数思想，使它把非线性问题转化为线性问题来解决，降低了算法的复杂度。
Chaos optimization algorithm is a global search method of selecting SVMs parameters, simulations show that the proposed method is an effective approach for parameter selection.
混沌优化算法是一种全局搜索方法，将混沌优化算法应用于支持向量机参数选取问题中，仿真表明该方法的有效性。
The prediction results will have direct effect on traffic control and traffic guidance. A traffic flow prediction model using support vector machines(SVMs) based method is proposed.
提出一种基于支持向量机的交通流量实时预测模型，通过采用序贯最小优化算法，能够实现对交通流量的有效预测。
The basic statistical learning theory (SLT) and its corresponding algorithms, support vector machines (SVMs), are surveyed, and especially, its latest research results are summarized and studied.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
The basic statistical learning theory (SLT) and its corresponding algorithms, support vector machines (SVMs), are surveyed, and especially, its latest research results are summarized and studied.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
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