在此，本文将支持向量机技术引入到烟叶自动分级中。
So Support Vector Machines is introduced in the Automatic Grading of Tobacco Leaf in the paper.
针对当前支持向量机计算效率的不足，提出了改进的并行支持向量机技术。
A new method of PSVM (Parallel Support Vector Machine) derived from traditional TSVM (Tactic Support Vector Machine) was proposed to improve calculating efficiency.
介绍统计学习理论和支持向量机，提出利用支持向量机技术进行似大地水准面拟合。
This paper introduces statistical learning theory and support vector machine, proposes a new method, support vector machine technology, to simulate quasi-geoid.
针对连铸过程的结晶器液面控制问题，提出了基于最小二乘支持向量机技术的预测模型的预测控制方法。
A new predictive control method based on least square support vector machine is proposed in order to control mold level of the continuous casting.
首次尝试将最小二乘支持向量机技术用于土壤侵蚀预测，并与BP神经网络的方法进行了对比，取得了较好的预测精度。
This paper try to predict soil erosion with the Least Square support vector machine technology and the better predict precision compared to the BP artificial neural network has been gotten.
分类部分，论文在理论上分析了文本分类采用支持向量机技术的优点，对两种具体的SVM算法-C-SVC和V-SVC进行了研究并利用实例进行验证。
The two classical SVM algorithms-C-SVC algorithm and S-SVC algorithm have been done more research and the two algorithms performance has been compared by using practice data.
支持向量机(SVM)是基于统计学习理论的新一代机器学习技术。
Support vector machine (SVM) is a new generation machine learning technique based on the statistical learning theory.
本文基于混合学习和集成学习的思想，将这两种方法应用于支持向量机建模技术中，主要解决预测分析问题。
This paper mainly focuses on the prediction problem by the application of hybrid and ensemble thinking into the modeling base on SVM.
支持向量机是一种基于结构风险最小化原理的学习技术，也是一种新的具有很好泛化性能的回归方法。
Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
基于支持向量机的理论和技术，构建了换档质量评价系统。
Based on the theory and technique of the support vector machines, 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。
Based on the theory and technique of the support vector machines (SVMs), an assessment system was built.
然而，支持向量机毕竟还是一种不够成熟的新技术，还存在许多局限性。
However, the support vector machine, after all, a kind of new technology is not mature enough, there are many limitations.
基于小波分解提取人脸特征技术和多分类支持向量机模型，提出了一种新的准正面人脸识别算法。
This paper presents a novel algorithm for quasi-frontal face recognition based on the wavelet decomposition technique and a multi-class Support Vector Machine (SVM) model.
文章采用一种新的模式识别技术——支持向量机（SVM），来进行高速公路的事件检测。
This paper presents the application of a recently-developed pattern classifier called support vector machine(SVM) in expressway incident detection.
核函数是SVM的关键技术，核函数的选择将影响着支持向量机的学习能力和泛化能力。
Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.
支持向量机作为数据挖掘的一项新技术，应用于模式识别和处理回归问题等诸多领域。
As new technology of data mining, support vector machines (SVM) have been successfully applied in pattern recognition and regression problem, et al.
支持向量机是数据挖掘的一项新技术，被认为是目前针对小样本的分类、回归等问题的最佳理论。
Support vector machine is a new technique of data mining, which is regarded as the best theory aimed at solving the problem of classification and regression of small sample pool at present.
以仙游县为例，探讨了将地理信息系统技术(GIS)和支持向量机(SVM)算法应用于滑坡灾害危险性评价的基本思路和技术路线。
By taking Xianyou County as an example, a new method for landslide hazard evaluation based on GIS and Support Vector Machines (SVM) is presented in this paper.
支持向量机方法能够解决小样本情况下非线性函数拟合的通用性和推广性的问题，是求复杂的非线性拟合函数的一种非常有效的技术。
The problems of universality and extensibility in nonlinear function approximation using small samples can be solved by the method, it a very efficient technique for nonlinear function approximation.
支持向量机是一种新型的模式识别技术。
Support vector machine (SVM) is a novel pattern recognition technique.
基于统计学习理论的支持向量机是一类新型的机器学习算法，由于它出色的学习性能，该技术已经成为当前学术界的研究热点。
The support vector machine based on statistical learning is a new type of machine learning algorithm, which has become the hot spot of academic study because of its excellent learning performance.
本文首先详细介绍了支持向量机的原理和算法，根据支持向量机的分类特性，提出将支持向量机应用到FSK解码技术中。
This paper introduces the theory and algorithm of SVM at first. It proposes to use the SVM to decode FSK signal based on the classifiable character of SVM.
提出了一种基于RASTA滤波技术的多维语音特征和支持向量机分类的VAD算法，适用于低信噪比情况下的话音检测。
VAD algorithm based on RASTA-filter multi-dimensional speech feature and Support Vector Machine is presented. It applies to the speech detection under the low SNR conditions.
支持向量机是继神经网络后机器学习的热点研究技术，它主要应用于分类和回归问题中。
SVM is the hot issue accompanying artificial neural network in machine learning. It involves any practical problems such as classification and regression estimation.
直推式支持向量机(TSVM)是一种直接从已知样本出发对特定的未知样本进行识别和分类的技术。
Transductive support vector machines (TSVM) classifies the new data vector based on the information only related to this data vector.
支持向量机和神经网络都是目前关于机器学习技术的研究热点。
Support vector machine (SVM) and the neural network are both currently hot subject in the area of machine learning technology.
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.
实验结果表明，结合核主成份分析的特征提取，支持向量机方法是一种很有前景的多目标图像分割技术。
This paper investigates the segmentation of multi-target image based on SVM approach combining feature extraction of kernel PCA.
本文在研究了众多边缘检测方法的基础上，重点研究了最小二乘支持向量机（LS-SVM）的图像边缘检测技术，提出了一种基于混合核函数最小二乘支持向量机的图像边缘检测方法。
On the basis of studying on least-squares support vector machines (LS-SVM) of the image edge detection technology, Proposed a new method, which is based on mixed Kernel LS-SVM image edge detection.
本文在研究了众多边缘检测方法的基础上，重点研究了最小二乘支持向量机（LS-SVM）的图像边缘检测技术，提出了一种基于混合核函数最小二乘支持向量机的图像边缘检测方法。
On the basis of studying on least-squares support vector machines (LS-SVM) of the image edge detection technology, Proposed a new method, which is based on mixed Kernel LS-SVM image edge detection.
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