This paper presents an adaptive and iterative support vector machine regression algorithm (CAISVR) based on chunking incremental learning and decremental learning procedures.
文中基于块增量学习和逆学习过程，提出了自适应迭代回归算法。
In this paper, a special support vector regression machine algorithm is proposed, within which smoothing function and method to solve LC1 type functions are combined to solve Newton-type algorithm.
分别采用光滑化函数法和求解lc1函数类型方法对牛顿型算法进行研究求解。
Based on the traditional support vector machine (SVM) for regression, a new learning algorithm of the improved SVM for regression is presented in this paper.
该文对用于回归估计的标准支持向量机(SVM)加以改进，提出了一种新的用于回归估计的支持向量机学习算法。
The algorithm promoted the study of a multi-output support vector regression machine and provided a novel means to solve the problem of time-dependent variational inequalities.
文中给出的多输出支持向量回归机不仅推进了多输出支持向量回归机的研究，而且为解决依赖时间的变分不等式问题提供了一种新思路。
The algorithm promoted the study of a multi-output support vector regression machine and provided a novel means to solve the problem of time-dependent variational inequalities.
文中给出的多输出支持向量回归机不仅推进了多输出支持向量回归机的研究，而且为解决依赖时间的变分不等式问题提供了一种新思路。
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