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。
Support Vector Machine for regression (SVR) has shown very good learning performance.
回归型支持向量机方法SVR具有很好的学习性能。
Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
支持向量机是一种基于结构风险最小化原理的学习技术，也是一种新的具有很好泛化性能的回归方法。
Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
A speaker verification system based on support vector regression machine (SVR) is presented in this paper.
提出一种基于支持向量回归机的说话者确认方法。
The support vector machine (SVM) is a very effective method for regression issue.
支持向量回归机是求解回归问题的新的十分有效的方法。
The support vector machine (SVM) is a linear classification machine, it is used commonly in the pattern recognition and nonlinear regression.
支持向量机(SVM)是一种线性机器，广泛用于模式分类和非线性回归。
When applied to regression and prediction, we often call SVM as support vector regression machine SVR.
当sVM用于回归分析和预测时，通常称其为支持向量回归机svr。
Support Vector Machine is an excellent learning technique, and it is also a class of regression method with a good generalization ability.
支持向量机是一种优秀的学习方法，也是具有很好泛化性能的回归方法。
For non-linear problem, the forecasting technique of pre-classification and later regression was proposed, based on the classification approach of Support Vector Machine (SVM).
针对非线性问题，提出了基于支持向量机分类基础的先分类、再回归的预测方法。
Support vector machine is a new technique of data mining, which is regarded as the best theory aimed at solving the problem of classification and regression of small sample pool at present.
支持向量机是数据挖掘的一项新技术，被认为是目前针对小样本的分类、回归等问题的最佳理论。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
Support vector machine (SVM) is an effective method for resolving regression problem, however, traditional SVM is very sensitive to noises and outliers in the training sample.
支持向量机(SVM)是解决回归问题的一种有效的方法，但传统的支持向量机对样本中的噪声和孤立点非常敏感。
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。
Now, how to design fast and efficient SVM algorithms applied to regression estimation becomes a great challenge in practical applications of support vector machine.
目前，如何设计快速有效的回归估计算法仍然是支持向量机实际应用中的问题之一。
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。
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。
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。
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