中文摘要：

参数调节问题是支持向量回归的基本问题,已有的参数调节方法主要采用内外双层优化框架，调节过程中，训练学习器与更新超参数交替进行,这种嵌套结构具有较高的计算复杂性．针对这一问题，提出了支持向量回归多参数的同时调节模型．首先，将Lagrange乘子、惩罚因子、不敏感度参数和核函数参数合并为一个参数向量，推导出支持向量回归问题的一个新的表示形式，可将原来分离的双层调节过程整合为一个单层调节过程．然后，应用贯序无约束极小化技术（SUMT），将支持向量回归问题转化为多元无约束优化问题,在此基础上，应用变尺度方法（VMM）设计、分析并实现了一个同时调节算法。最后，通过标准数据集上的实验，验证了同时调节算法的收敛性，并比较了同时调节算法与常用调节算法的有效性和计算效率。理论分析与实验结果表明，同时调节模型是一正确且有效的多参数调节模型。

英文摘要：

Parameter tuning is fundamental for support vector regression （SVR）. There are three types of parameters we focus on. The first is the insensitive factor ε. SVR uses the E-insensitive loss function which does not penalize errors below some ε. The second is the penalty factor C, which is a compromise between the model complexity and the empirical risk. The third is the kernel function parameter, usually, the radius basis function is considered, so the parameter is σ. Previous tuning methods mainly adopted a nested two layer optimization framework. In this framework, the inner layer optimizes the Lagrange multipliers α, and the outer layer makes use of these Lagrange multipliers to optimize penalty factors C, insensitive factors ε and kernel parameters σ. The parameters and hyperparameters were trained alternately, which directly led to high computational complexity. To solve this problem, we propose a simultaneous tuning model for multiple parameters of SVR. First, we combine Lagrange multipliers, penalty factors, insensitive factors and kernel parameters into one vector, and derive a new optimization formula for SVR, which converts the two separate tuning processes into one optimization process. Then, we transform the optimization formula into one unconstraint multivariate optimization problem through sequential unconstrained minimization technique （SUMT）. Based on these theoretical results, we design, analyze and implement an algorithm for the simultaneous tuning model with variable metric method （VMM）. Finally, by experiments on benchmark datahases, we verify the convergence of the simultaneous tuning algorithm, and compare the accuracy and efficiency of the algorithm with that of common tuning algorithms. Theoretical and experimental results show that the simultaneous tuning model is valid and efficient.