1) multiple support vector machine
多支持向量机
1.
Monitoring model based on kernel principal component analysis and multiple support vector machines and its application
基于核主元分析与多支持向量机的监控诊断方法及其应用
2.
Based on the idea that the accuracy of model could be significantly improved by combining several sub-models,a multiple support vector machine(MSVM)modeling approach was proposed to buil.
基于多个模型的组合可以提高模型精度和鲁棒性的思想,提出多支持向量机(MSVM)组合模型的软测量建模方法。
2) multiclass SVM
多类支持向量机
1.
In this paper,a method of decision fusion based on multiclass SVM and D-S evidential theory is presented to try to solve the problem,multiclass SVM is used as the local classifier, and the BPAF is constructed accordingly,then the primary results are fused to obtain the ultima result using D-S evidential theory.
本文提出了一种基于多类支持向量机和D-S证据理论的决策融合算法,将多类支持向量机作为局部判决器,构造了相应的基本概率赋值函数,然后用D-S证据理论对各初步判决结果进行融合,得出对目标的最终识别结论。
3) Multi-class Support Vector Machine
多类支持向量机
1.
Data fusion strategies for small sample based on multi-class support vector machine
以多类支持向量机为基础的小样本信息融合策略
2.
A new method of image classification based on wavelet transformation and multi-class support vector machine is proposed,which employs wavelet transformation to extract features of the original images and then classifies them by multi-class support vector machine.
提出了一种基于小波变换和多类支持向量机的图像分类新方法,该方法利用小波变换进行图像特征提取,利用多类支持向量机进行图像分类,并与基于图像底层特征的图像分类方法进行了实验比较。
4) multi-class SVM
多类支持向量机
1.
Application of modified wavelet features and multi-class SVM to pathological vocal detection;
基于小波特征和多类支持向量机的病态语音识别方法
2.
New multi-class SVM algorithm based on one-class SVM;
一种新的多类支持向量机算法
3.
For indicating the existence and attack intensity of DDoS attack simultaneously,multi-class SVM (MCSVM) is introduced to detecting DDoS Attacks.
为了在指示攻击存在的同时,也指示攻击强度,多类支持向量机(MCSVM)被引入到DDoS检测中。
5) Multi-kernel support vector machine
多核支持向量机
6) multi-layer SVM
多层支持向量机
1.
This paper tries to adopt SMO algorithm to establish a multi-layer SVM text categorization system.
论文将SMO分类算法结合到文本分类研究中,通过构建多层支持向量机文本分类树,实现了基于SMO的多层次文本分类系统。
补充资料：支持向量机方法
支持向量机(SVM)是90年代中期发展起来的基于统计学习理论的一种机器学习方法，通过寻求结构化风险最小来提高学习机泛化能力，实现经验风险和置信范围的最小化，从而达到在统计样本量较少的情况下，亦能获得良好统计规律的目的。支持向量机算法是一个凸二次优化问题,能够保证找到的极值解就是全局最优解，是神经网络领域域取得的一项重大突破。与神经网络相比，它的优点是训练算法中不存在局部极小值问题，可以自动设计模型复杂度(例如隐层节点数)，不存在维数灾难问题，泛化能力强。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条