共查询到18条相似文献,搜索用时 62 毫秒
1.
为了解决最小二乘支持向量机模型稀疏性不足的问题,提出了一种约简核矩阵的LS-SVM稀疏化方法.按照空间两点的欧式距离寻找核矩阵中相近的行(列),并通过特定的规则进行合并,以减小核矩阵的规模,进而求得稀疏LS-SVM模型.以高斯径向基核函数为例,详细阐述了改进方法的实现步骤,并通过仿真表明了采用该方法求得的稀疏LS-SVM模型泛化能力良好. 相似文献
2.
3.
4.
针对最小二乘支持向量机缺乏稀疏性的问题,提出了一种基于边界样本的最小二乘支持向量机算法。该算法利用中心距离比来选取支持度较大的边界样本作为训练样本,从而减少了支持向量的数目,提高了算法的速度。最后将该算法在4个UCI数据集上进行实验,结果表明:在几乎不损失精度的情况下,可以得到稀疏解,且算法的识别速度有了一定的提高。 相似文献
5.
提出了一个最小二乘双支持向量回归机,它是在双支持向量回归机基础之上建立的,打破了标准支持向量回归机利用两条平行超平面构造ε带的思想。事实上,它是利用两条不一定平行的超平面构造ε带,每条超平面确定一个半ε-带,从而得到最终的回归函数,这使该回归函数更符合数据本身的分布情况,回归算法有更好的推广能力。另外,最小二乘双支持向量机只需求解两个较小规模的线性方程组就能得到最后的回归函数,其计算复杂度相对较低。数值实验也表明该回归算法在推广能力和计算效率上有一定的优势。 相似文献
6.
7.
8.
最小二乘双支持向量回归机(LSTSVR)通过引入最小二乘损失将双支持向量回归机(TSVR)中的二次规划问题简化为两个线性方程组的求解,从而大大减少了训练时间。然而,LSTSVR最小化基于最小二乘损失的经验风险易导致以下不足:(1)“过学习”问题;(2)模型的解缺乏稀疏性,难以训练大规模数据。针对(1),提出结构化最小二乘双支持向量回归机(S-LSTSVR)以提升模型的泛化能力;针对(2),进一步利用不完全Choesky分解对核矩阵进行低秩近似,给出求解S-LSTSVR的稀疏算法SS-LSTSVR,使模型能有效地训练大规模数据。人工数据和UCI数据集中的实验证明SS-LSTSVR不但可以避免“过学习”,而且能够高效地解决大规模训练问题。 相似文献
9.
鉴于传统在线最小二乘支持向量机在解决时变对象的回归问题时, 模型跟踪精度不高, 支持向量不够稀疏, 结合迭代策略和约简技术, 提出一种在线自适应迭代约简最小二乘支持向量机. 该方法考虑新增样本与历史数据共同作用对现有模型产生的约束影响, 寻求对目标函数贡献最大的样本作为新增支持向量, 实现了支持向量稀疏化, 提高了在线预测精度与速度. 仿真对比分析表明该方法可行有效, 较传统方法回归精度高且所需支持向量数目最少. 相似文献
10.
11.
Sparse representation based on projection method in online least squares support vector machines 总被引:1,自引:0,他引:1
A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines (LS-SVM). The new inputs are projected into the subspace spanned by previous basis vectors (BV) and those inputs whose squared distance from the subspace is higher than a threshold are added in the BV set, while others are rejected. This consequently results in the sparse approximation. In addition, a recursive approach to deleting an exiting vector in the BV set is proposed. Then the online LS-SVM, sparse approximation and BV removal are combined to produce the sparse online LS-SVM algorithm that can control the size of memory irrespective of the processed data size. The suggested algorithm is applied in the online modeling of a pH neutralizing process and the isomerization plant of a refinery, respectively. The detailed comparison of computing time and precision is also given between the suggested algorithm and the nonsparse one. The results show that the proposed algorithm greatly improves the sparsity just with little cost of precision. 相似文献
12.
13.
针对递归最小二乘支持向量机的递归性易导致建模中偏微分方程组求解困难的问题,提出用解析法求解偏微分方程组,实现了完整的递归最小二乘支持向量机模型.首先分析了各参数的相关性,然后推导出偏微分方程的解析表达式并求解.仿真实例表明,在动态系统建模中,该模型的性能比常用的串并联模型以及现有不完整递归最小二乘支持向量机模型的精度更高、性能更好. 相似文献
14.
一种快速稀疏最小二乘支持向量回归机 总被引:4,自引:0,他引:4
将Jiao法直接应用于最小二乘支持向量回归机上的效果并不理想,为此采用不完全抛弃的策略,提出了改进的Jiao法,并将其应用于最小二乘支持向量回归机.数据集测试的结果表明,基于改进Jiao法的稀疏最小二乘支持向量回归机,无论在支持向量个数和训练时间上都取得了一定的优势.与其他剪枝算法相比,在不丧失回归精度的情况下,改进的Jiao法可大大缩短训练时间.另外,改进的Jiao法同样适用于分类问题. 相似文献
15.
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controUed plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm. 相似文献
16.
17.
Combining reduced technique with iterative strategy, we propose a recursive reduced least squares support vector regression. The proposed algorithm chooses the data which make more contribution to target function as support vectors, and it considers all the constraints generated by the whole training set. Thus it acquires less support vectors, the number of which can be arbitrarily predefined, to construct the model with the similar generalization performance. In comparison with other methods, our algorithm also gains excellent parsimoniousness. Numerical experiments on benchmark data sets confirm the validity and feasibility of the presented algorithm. In addition, this algorithm can be extended to classification. 相似文献
18.
As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. 相似文献