阐述了模糊支持向量机的原理。
然后基于支持向量机进行分类建模和预测。
Then, we realize classification modeling and forecasting test based on SVM.
验证支持向量机用于手写数字识别的有效性。
Validating the effectiveness of support vector machine for handwritten digit recognition.
提出了一种基于支持向量机的改进的降维方法。
In this paper we present an improved dimensionality reduction method based on support vector machines.
然后基于支持向量机进行分类建模和预测过程。
Then, we implement classification modeling and forecast based on SVM.
论文将支持向量机引入到动态电能质量分类问题中。
This paper presents a Support Vector Machine (SVM) method for classification of dynamic power quality disturbances.
提出了电液伺服系统的支持向量机的辨识建模方法。
A method of the electro hydraulic servo system identification modeling is presented based on support vector machines.
支持向量机(SVM)是一种新型的机器学习方法。
Support vector machine (SVM) is a new machine learning technique.
本文首次采用支持向量机方法对医学图像进行了分类研究。
This paper firstly investigates the application of support vector machines for medical image classification.
论文对支持向量机（SVM）和正交设计方法进行了比较。
The purpose of this paper is to provide a discussion and comparison of SupportVector Machine (SVM) and the orthogonal design method.
进而提出了基于支持向量机(SVM)的网络延时预测方法。
The prediction method of network delays based on support vector machine (SVM) was put forward.
提出了一种最小二乘支持向量机的电池剩余电量预测新模型。
A novel prediction model for remaining capacity of batteries based on least square support vector machine (LS-SVM) was proposed.
还讨论支持向量机用于人脸识别的主要处理流程和识别框图。
It also discusses the primary processing and recognition diagram of SVM applied to face recognition.
论文将支持向量机的机器学习方法引入到医学图像的分类问题。
In this thesis, SVM as a new machine learning method is brought into medical image classification.
支持向量机（SVM）是根据统计理论提出的一种新的学习算法。
The support vector machine(SVM) is a new learning technique based on the statistical learning theory.
提出了基于支持向量机的分类原理对鸢尾属植物进行分类的方法。
This article presents a new method of plant classification of iris based on the support vector machine classification principle.
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。
Experimental results indicate that it is feasible to adopt SVMs for stressed speech classification.
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.
这种学习可以使用神经网络或者支持向量机，不过用决策树也可以实现类似的功能。
This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.
最后一个主题将讲解支持向量机分类物中核心使用的可训练物件侦测系统。
The last topic is about a trainable object detection system using at its core a Support Vector Machine classifier.
支持向量机用作说话人确认模型来训练目标说话人和背景说话人的语音数据。
The support vector machine based speaker verification models are trained on the enrolled speaker and the background model.
介绍了一种基于支持向量机的混合气体红外光谱组分浓度和种类分析的新方法。
A new method of infrared spectrum analysis based on support vector machine for mixture gas was proposed.
将支持向量机方法应用到地基工程中，提出了计算地基承载力的支持向量机方法。
Applied support vector machine to foundation engineering, and proposed support vector machine method to compute the foundation bearing capacity.
一个支持向量机的支持向量数相关的VC维是怎样的？有一个公式，关于这两个量？
How is the VC dimension of a support-vector machine related to its number of support vectors? Is there a formula relating these two quantities?
提出支持向量机-模糊预测控制方法，介绍支持向量机在列车启动控制过程中的应用。
It is proposed a fuzzy forecast control method based on support vector machine. The applications of the machine to the train start-up control are given.
目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
提出了一种基于最小二乘支持向量机(LS - SVM)的铁路客运量预测的新方法。
A new prediction approach for railway passenger volume is put forward by means of Least Squares Support Vector Machine (LS-SVM).
大部分数字识别的工作可以由神经网络来完成，但最近支持向量机也被证明可以在分类方面做得更好。
Much of the work on digit recognition has been done in the neural network community, but more recently support vector machines have proven to be even better classifiers.
大部分数字识别的工作可以由神经网络来完成，但最近支持向量机也被证明可以在分类方面做得更好。
Much of the work on digit recognition has been done in the neural network community, but more recently support vector machines have proven to be even better classifiers.
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