Structural Risk Minimization

They can solve small-sample learning problems better by using structural risk minimization in place of experiential risk minimization .

由于使用结构风险最小化原则代替经验风险最小化原则，使它较好的解决了小样本情况下的学习问题。

Radial Basis Function Networks Based on Structural Risk Minimization Principle

基于结构风险最小化原则的径向基函数网络

Structural Risk Minimization Method for Wavelet Neural Network Learning

小波神经网络学习的结构风险最小化方法

Structural Risk Minimization Principle Based on Complex Random Samples

基于复随机样本的结构风险最小化原则

In this paper , another method is given to implement the structural risk minimization principle .

给出实现结构风险最小化原理（最大边缘）的另一种方法。

Structural risk minimization principle based gaussian mixture modeling

基于结构风险最小化准则的高斯混合模型的参数估计

SVM is based on the principle of structural risk minimization , with good generalization ability .

支持向量机以结构风险最小化为原则，推广泛化能力强。

An algorithm is presented through using structural risk minimization ( SRM ) based on statistical learning theory .

在研究统计学理论的基础上，提出了以结构风险最小化为目标的训练方法。

SVM is a novel machine learning method , based on structural risk minimization principle and has excellent learning performance .

支持向量机（SVM）是一类新型机器学习方法，基于结构风险最小化归纳原则，具有出色的学习能力。

Rejecting Nearest Neighbor Classifier Based on Structural Risk Minimization Principle Self-organization Multiple Region Covering Model

基于SRM自组织多区域覆盖的可拒绝近邻分类算法研究

The SVM ( support vector machines ) is a classification technique based on the structural risk minimization principle .

SVM（SupportVectorMachines）是一种基于结构风险最小化原理的分类技术。

Analysis of the limitations of empirical risk minimization principle and introduce the advantages of structural risk minimization principle .

分析经验风险最小化的局限性，介绍结构风险最小化原则及其优越性，详细总结支持向量机理论和研究现状。

Structural Risk Minimization for Controlling Generalization Performance of Rough Set Learning Machine flyers for roving frames

粗糙集学习机器泛化性能控制的结构风险最小化方法

The support vector machines based on structural risk minimization has tremendous advantages in small samples and better generalization ability .

支持向量机算法是基于结构风险最小化原则的，在小样本情况下具有很大的优势，有较好的泛化推广能力。

As for structural risk minimization principle , SVM has a better solution to small sample , nonlinear , high-dimensional learning problems .

由于依据结构风险最小化原则，SVM较好地解决了小样本、非线性、高维学习问题，成为了当前数据挖掘领域和机器学习界的研究热点。

As Support Vector Machine ( SVM ) bases on Structural Risk Minimization ( SRM ), it can solve the problems .

而基于支撑矢量机的调制分类器采用结构风险最小化原则，在样本有限情况下仍能达到较好性能。

Analysis of support vector machine principle of empirical risk minimization and structural risk minimization principle , then introduces the optimal SVM classification surface .

分析了支持向量机的经验风险最小化原则和结构风险最小化原则，然后介绍了SVM的最优分类面。

Support vector machines have excellent learning , classification ability and generalization ability which use structural risk minimization instead of empirical risk minimization .

支持向量机是采用结构风险最小化原则代替传统统计学中的，基于大样本的经验风险最小化原则的新型机器学习方法，具有出色的学习分类能力和推广能力。

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

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。

Support vector machine , which based on statistical learning theory and structural risk minimization principle , is of the good extension ability and the better accuracy .

支持向量机方法基于统计学习理论与结构风险最小化原理，具有良好的推广性和较高的准确率。

Support vector machine ( SVM ) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set .

基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。

Compared with traditional learning method , SVM employs structural risk minimization criterion to minimize the learning error and simultaneously decrease the generalization error .

该方法采用了结构风险最小化原则，与传统机器学习方法相比，在最小化学习误差的同时可以保证有较小的泛化误差。

To improve the forecast ability of highway freight traffic , SVR based on structural risk minimization was applied to forecasting highway freight traffic .

为了提高公路货运量预测的能力，应用基于结构风险最小化准则的标准支持向量回归机方法来研究公路货运量预测问题。

Statistical learning theory ( SLT ) which is based on the structural risk minimization provides a new idea to the research of intelligent and scientific project risk management .

统计学习理论为项目风险管理向智能化、科学化方向发展提供了一个新的思路。

It operates on a principle , called structural risk minimization , which aims to minimize the upper bound on the expected generalization error .

它基于结构风险最小化准则，目的是最小化泛化误差上界。

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

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。

Support Vector Machines ( SVM ) adopts structural risk minimization principle and kernel method . It is a simple quadratic programming and has a unique solution .

支持向量机模型采用结构风险极小化原则和核函数方法来构造分类模型，模型比较简单，解具有唯一性。

Structural risk minimization induce principle is used to control the bound on the value of achieved risk by controlling experiential risk and belief bound at the same time .

结构风险最小化归纳原则通过控制经验风险和置信范围来控制实际风险的界。

In this paper , we present an estimation of the parameters of Gaussian mixture models based on Structural Risk Minimization principle which is the key principle of statistical learning theory .

本文基于统计学习理论中结构风险最小化准则，导出了高斯混合模型的参数估计公式，与基线系统和其它成分数的选择方法相比，有较好的效果。

Based on the elementary idea of kernel mapping and the principle of structural risk minimization , SVR transforms the regression problem into a quadratic programming problem .

支持向量回归采用核映射的基本思想，基于结构风险最小化原则，将回归问题转化为一个二次规划问题。