To apply the discriminative classifier in the speaker recognition, the building sequence kernel support vector machine(SVM) becomes the trend in the field.
为了更好地将区分式分类方法应用于说话者确认系统中，构建序列核支持向量机已成为说话人识别领域的研究热点与趋势。
The selection of the kernel function parameter and error penalty factor affected the precision of the support vector machine (SVM) significantly.
核函数参数和误差惩罚因子的选择对支持向量机模型（SVM）的精度有较大影响。
For this reason, several important properties of kernel function are discussed on the basis of support vector machine.
为此，在研究支持向量机的基础上，给出了核函数的若干重要性质。
On the basis of analysis of several methods for modeling, a soft sensor based on kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed.
在具体分析了多种建模方法的基础上，提出了核主元分析结合最小二乘支持向量机软测量建模方法。
The SVM (Support vector Machine) classifies the data by mapping the vector from low-dimensional space to high-dimensional space using kernel function.
而SVM(支持向量机)引进核函数隐含的映射把低维特征空间中的样本数据映射到高维特征空间来实现分类。
Kernel-based Support Vector Machine (SVM) is widely used in many fields (e. g. image classification) for its good generalization, in which the key factor is to design effective kernel functions.
基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用，运用支持向量机的关键是设计有效的核函数。
This paper presents the Support Vector Machine and its application to modeling in the polyester industry. Several SVM Algorithms and their kernel functions are presented and compared.
本文主要讨论支持向量机方法在聚酯工业过程软测量建模中的应用，分析各类支持向量机算法、 参数及核函数的选择对建模精度的影响。
Therefore it is of great significance to study the properties of kernel function of support vector machine.
因此研究支持向量机的核函数性质，对于寻找核函数有重要意义。
The dissertation mainly aims at applying support vector machine (SVM) and kernel principal component analysis (KPCA) to intrusion detection.
本文的主要工作是将支持向量机(SVM)及核主成分分析(KPCA)应用到入侵检测技术中。
The dissertation mainly aims at applying support vector machine (SVM) and kernel principal component analysis (KPCA) to intrusion detection.
本文的主要工作是将支持向量机(SVM)及核主成分分析(KPCA)应用到入侵检测技术中。
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