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。
They are more efficient than inductive SVMs, especially for very small training sets and large test sets.
它在包含少量有标签样本的训练集和大量无标签样本的测试集上，具有良好的效果。
This allows a flexible way to constructing various kernels for use with the support vector machines(SVMs).
在这个框架下，我们可以更灵活的构造不同的核函数为支持向量机的输入。
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。
Support Vector Machines(SVMs)is an algorithm of machine learning, which was widely used in many fields abroad.
支持向量机是一种机器学习算法，在国外已广泛应用于工程实践领域。
Through support vector machine algorithms for data classification training, SVMs provide a effective way for analysis of this data.
通过支持向量机训练算法对数据进行分类训练，为分析数据提供有效的手段。
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。
Directed acyclic graph support vector (DAG - SVMS) multi - category classification methods, is a new multi - category classification methods.
有向无环图支持向量(DAG-SVMS)多类分类方法，是一种新的多类分类方法。
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。
Second, we explore the interdependences between subcellular locations and incorporates them with SVMs for prediction of protein subcellular localization.
其次，我们探索了亚细胞位置之间的依赖关系，并且将这种关系用于支持向量机来进行蛋白质亚细胞定位。
Many algorithms are used to create supervised learners, the most common being neural networks, Support Vector Machines (SVMs), and Naive Bayes classifiers.
创建监管学习程序需要使用许多算法，最常见的包括神经网络、SupportVectorMachines (SVMs)和Naive Bayes分类程序。
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。
Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。
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。
Support vector machines (SVMs) which suit to classification problem for tiny samples is designed for different shot types through the features extracted by the method.
利用所提出的特征，采用适合小样本分类问题的支持向量机(SVM)对足球视频镜头分类。
Our method that can be applicable to the set of nonlinear hypothesis functions as well as the set of linear ones generalizes the theory about the capacity control of SVMs.
该方法不仅能用支撑矢量核函数而且可以采用其他的函数作为基函数。
We discuss bagging and boosting and suggest some plausible justification for their success. We also describe some recent work about combining SVMs in a way similar to bagging.
我们将讨论拔靴集成法与多模激发法，以及这两个演算法是如何成功的被运用。我们也将介绍近来运用与拔靴集成法相似的方法，结合支持向量机所做的一些案例。
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 result indicates, that the accuracy of predictions by SVMs is better then the predictions of BP neural networks, and with some merits of small samples, multi-dimensions and non-linear.
结果表明，支持向量机比BP神经网络有较高的预测精度，并且具有小样本、高维数及非线性等优点。
The result indicates, that the accuracy of predictions by SVMs is better then the predictions of BP neural networks, and with some merits of small samples, multi-dimensions and non-linear.
结果表明，支持向量机比BP神经网络有较高的预测精度，并且具有小样本、高维数及非线性等优点。
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