统计学习理论

支撑矢量机（SVM，supportVectorMachine）是基于统计学习理论的一种模式识别方法。

Support vector machine is a pattern recognition algorithm based on statistical learning theory .

统计学习理论（Statisticallearningtheory或SLT）是研究有限样本情况下机器学习规律的理论。

Statistical learning theory is a small-sample statistics theory .

支持向量机（SupportVectorMachines，简称SVM）是在统计学习理论的基础上发展起来的一种新的通用学习方法，它已初步表现出很多优于已有方法的性能。

Support Vector Machines ( SVM ) is a kind of novel machine learning methods .

统计学习理论（Statisticallearningtheory或SLT）是一种专门研究小样本情况下机器学习规律的理论，它具有完备的理论基础。

Statistical Learning Theory ( SLT ) is a theory , which research the machine study specially in small samples .

支持矢量机（SupportVectorMachines，简称SVM）是基于统计学习理论的一种新的模式识别技术。

Support Vector Machines ( SVM ) is a new pattern recognition technology , which is based on Statistical Learning Theory .

在统计学习理论中，尤其对于分类问题，VC维扮演着中心作用。

VC dimension plays a central role in the Statistical Learning Theory especially for classification problems .

支持向量机（supportVectorMachine，SVM）是一类基于统计学习理论的新型机器学习方法。

Support vector machines ( SVMs ) is a new kind of machine learning method based on statistical learning theory , which has many advantages .

研究基于统计学习理论的支持向量机（SVM）回归在汛期旱涝预测中的应用。

The application of SVM regression method on Zhejiang drought and flood 's forecast at flood season was studied .

在统计学习理论基础上发展的通用学习方法支持向量机（supportVectorMachineSVM）能够较好地解决实际中的小样本学习问题。

The support vector machine is a new universal learning method based on statistic learning theory , it can effectively resolve the problem of small sample in practice .

为了更好地说明统计学习理论在实际中的实现问题，我们回顾了SVM的基本概念及基本理论。

In order to explain the implementation problem of statistical learning theory , basic concepts and theory of the SVM ?

支持向量机是基于VC维和统计学习理论理念的数据挖掘中的一种新方法。

Support Vector Machine is a new method based on the idea of VC dimension and Statistical Learning Theory in data mining .

支持向量机（supportVectorMachine或简称SVM）是在统计学习理论的基础上研发出来的一种新的有效的机器学习方法。它的推广能力很强。

Support Vector Machine ( SVM ) is a new powerful machine learning method which developed in the framework of Statistical Learning Theory ( SLT ) . It has high generalization .

重点介绍了统计学习理论的三个核心概念：VC维、推广能力的界和结构风险最小化。

It mainly introduces three core concepts of STL , which are VC dimension , minimizing the bound by minimizing hand structural risk minimization .

在探索手写字符识别的方法上采用了统计学习理论，利用支撑向量机SVM作为基本的识别工具。

Support Vector Machine ( SVM ) is used as the implementation basis , which is a tool of Statistical Learning Theory ( SLT ) .

支持向量机是以统计学习理论为基础，建立在VC维和结构风险最小化原则之上的一种人工智能方法。

SVM is a method of machine learning according to the statistical learning theory . It is based on VC dimension and structural risk minimization principle .

领域溶合算法光滑了覆盖领域的分类边界，简化了SVM问题求解的复杂度，提高了覆盖算法的性能，将覆盖算法与统计学习理论结合起来，为覆盖算法提供了理论依据。

The fusion algorithm not only simplifies the solution of SVM and improves the performance of covering algorithm but also provides academic foundation for covering algorithm .

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。

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 .

针对传统的基于经验风险最小化信号消噪方法和现有的小波阈值信号消噪方法的不足，基于统计学习理论，提出了一种改进的VC维小波包信号消噪方法。

A modified denoising method based on VC dimension and wavelet package is presented , improving the shortcomings of denoising methods based on empirical risk minimization and wavelets thresholds .

然后详细阐述了统计学习理论的基本理论，包括VC维理论、推广性的界和结构风险最小化；接下来介绍了最优超平面及其构造；

At first , basic theory of SLT including the VC dimension , the bound of extending and the principle of structural risk minimization ( SRM ) are presented .

针对这种实际情况，第三章从统计学习理论和数据融合角度出发，提出了一种基于最小均方支持向量学习机（LeastSquareSupportVectorMachine，LS-SVM）的蜂窝网络定位模型。

Considering this case , we propose a cellular network-aided positioning model based on Least Square Support Vector Machine ( LS-SVM ) from the view of statistics learning theory and data fusion in Chapter 3 .

支持向量机（SupportVectorMachines，简称SVMs）是建立在统计学习理论的VC（Vapnik-Chervonenkis）维理论和结构风险最小原理基础上的一种机器学习方法。

Support Vector Machines ( SVMs ) is a new learning machine built on VC ( Vapnik-Chervonenkis ) dimension and Structural Risk Minimum principle of SLT .

关于混沌时间序列的建模也有很多训练模型，其中最小二乘支持向量机（SVM）理论是一种新的基于统计学习理论的分类和回归工具，而且泛化能力强。

Least Squares Support Vector Machine ( LS-SVM ) theory is a new classification and regression tool based on statistical learning theory , which has strong the generalization ability .

我们也利用统计学习理论分析了实验数据，结果表明贪婪分阶段支持向量机的成功在于它能够产生较小的VC维。

Finally , we employ statistical learning theory to analyze the empirical results , which shows that the success of GS-SVMs owes to its ability of bringing small VC dimension .

支持向量机（SVM）衍生于统计学习理论，能够在最小化训练误差和模型复杂度之间找到最佳平衡点，是一种比较经典的机器学习方法。

Support vector machine ( SVM ), one of novel machine learning methods , is to find a fine balance between the training error and the complexity of the learning machine .

基于统计学习理论的CHMM结构优化分析

Analysis Based on SLT of the Optimization of HMM

SVM是在统计学习理论基础上发展起来的新的学习方法，在解决小样本、非线性及高维模式中有独特的优势，是机器学习领域的一个研究热点。

SVM is a novel learning method based on statistic learning theory and has been a hot issue in machine learning area in solving small sample , nonlinear and high dimensional pattern problems .

本文通过对统计学习理论中一些重要结论，特别是线性函数VC维数的分析，得到了一种线性规划支撑矢量机，包括线性规划线性支撑矢量机和线性规划非线性支撑矢量机。

Based on analysis of the conclusions in the statistical learning theory , especially the VC dimension of linear functions , linear programming SVMs are presented , including linear programming linear and nonlinear SVMs .

其次，认真研究了统计学习理论的主要内容和SVM算法的基本原理。并且就SVM的训练算法、分类算法、多类分类算法、核函数等热点问题分别加以讨论。

Secondly , the text studies the Statistical Learning Theory ( SLT ) and Support Vector Machine ( SVM ) theory seriously , discusses training , categorizing and multi-category classification algorithm and kernel function .

同时支持向量机（SVM）以统计学习理论为基础，在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，正在成为机器学习领域新的研究热点。

SVM , based on statistics science , displaying unique advantages in solving little sample problems , and non-linearity and high-dimensional patterns recognition problems , is becoming a newly active area in machine learning .

概述了统计学习理论中关于小样本统计的部分重要结论，详细地介绍了SVM的基本原理、算法、特点以及存在的问题，并讨论了它与统计学习理论中相关结论的关系；

The author 's major works are as the following : 1 The author briefly summarized some important conclusions of Statistic Learning Theory and discussed in detail the principle of SVM in pattern recognition .