1) weighted support vector machine algorithm
加权支持向量机算法
1.
Several kinds of improved support vector machine(SVM) algorithm such as increment learning algorithm,SMO,weighted support vector machine algorithm applied to large scale databases are introduced,to speed up the rate of exercise and to lower the radio of classification mistakes etc are analyzed.
介绍了增量学习算法、序列最小优化算法、加权支持向量机算法等几种应用于大型数据库,在加快训练速度、降低分类错误率等方面有改进的SVM流行算法。
2) weighted support vector machine
加权支持向量机
1.
Forecasting of the urban registered unemployment rate in Fujian province based on kernel principal component analysis and weighted support vector machine
基于核主成分与加权支持向量机的福建省城镇登记失业率预测
2.
The prediction model using the weighted support vector machine was proposed considering the factors which had relations with the global production indices.
以某选矿厂为实际背景,在分析了指标之间关系的基础上,采用加权支持向量机建立了相关指标的预报模型,并通过构造重要性函数的方法确定了支持向量机加权系数,最后利用该厂历史数据进行了仿真实验。
3.
So a new hybrid prediction model based on PCA and weighted support vector machine(WSVM) is proposed.
针对网络化制造资源配置受多因素影响,变化趋势复杂,难以用单一预测方法进行有效预测的问题,提出一种新的基于主成分分析和加权支持向量机的智能混合预测模型。
3) weighted support vector machines
加权支持向量机
1.
Using the high generaliza-tion ability of support vector machines (SVMs) and the idea of locally weighted learning (LWL) algorithm, this paper proposes a novel learning algorithm named weighted support vector machines (W_ SVMs) which is suitable for local le.
本文利用支持向量机(SVMs)泛化能力强的特点,结合局部加权学习(LWL)算法思想,提出一种适于局部学习的加权支持向量机(W-SVMs)学习算法和基于这种算法的移动建模方法。
2.
To deal with the common unbalanced problem in ABC inventory classification,this paper analyzed the limitation of general support vector machines and proposed a method of muhicriteria classification of inventories based on weighted support vector machines(W-SVM),which introduced weight factor for each class to compensate the bias towards class with large size of samples by unbalanced training set.
针对 ABC 库存分类中存在的样本类别不平衡问题,提出了一种基于加权支持向量机的多准则库存分类方法,通过引入类权重因子来解决由于训练集中的类别差异引起的分类结果偏向多样本类的问题。
4) support vector machine
支持向量机算法
1.
A newly proposed technique of data mining, called "support vector machine" , is suitable for the data mining of ill-posed.
近年来新提出的“支持向量机算法”适合于处理不适定问题,能限制过拟合,且因采用核函数算法,能有效处理非线性数据集,与当前化学化工中应用极广的人工神经网络相比,优越性明显,在化学化工中具有巨大的应用潜力。
2.
And the data of Al, Ca, Cu, Zn, Mg contents have been processed by a new data mining algorithm, support vector machine proposed by Vapnik, in order to search the correlation between these data and high blood pressure disease.
计算表明:支持向量机算法建模的正确分类率和留一法预报正确率均较Fisher法和KNN法等传统的模式识别算法高。
3.
By using leaving-one method, it has been found that the prediction ability of the mathematical modei made by support vector machine is better than that made by Fisher method or KNN method.
本工作应用支持向量机算法总结实验数据的数学模型,并用留一法检验模型的预报能力。
5) dual-weight support vector machines
双重加权支持向量机
6) GWSVM
广义加权支持向量机
1.
A classification algorithm of generalized weighted support vector machine(GWSVM) is put forward.
提出了一种广义加权支持向量机(GW SVM)的焊接缺陷分类算法。
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