介绍了最小二乘支持向量机计算法和滑动时间窗的建立。
The establishment of beast square support vector machine method and sliding time window is introduced.
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。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
并且采用了最小二乘支持向量机，用等式约束取代了支持向量机中的不等式约束，降低了运算量，提高了学习效率。
The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved.
本文在研究了众多边缘检测方法的基础上，重点研究了最小二乘支持向量机（LS-SVM）的图像边缘检测技术，提出了一种基于混合核函数最小二乘支持向量机的图像边缘检测方法。
On the basis of studying on least-squares support vector machines (LS-SVM) of the image edge detection technology, Proposed a new method, which is based on mixed Kernel LS-SVM image edge detection.
实现了基于数据挖掘理论和最小二乘支持向量机短时预测的多接口远程智能供水监管系统。
A short-term prediction system based on data mining preparation and least squares support vector machine was presented for a multi-port remote monitoring and management system.
基于贝叶斯证据框架下的最小二乘小波支持向量机，设计了一种新型模拟电路故障诊断方法。
Based on least squares wavelet support vector machines (LS-WSVM) within the Bayesian evidence framework, a systematic method for fault diagnosis of analog circuits was proposed.
在具体分析了多种建模方法的基础上，提出了核主元分析结合最小二乘支持向量机软测量建模方法。
On the basis of analysis of several methods for modeling, a soft sensor based on kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed.
针对基态趋势客流预测问题，研究了进行奇异值检测处理并运用最小二乘支持向量机进行预测的解决方案。
In the study of time series forecasting in ground states, the method for recognition and processing singular values is proposed, then LS-SVM is applied to forecast.
本文研究了基于最小二乘支持向量机的软测量建模方法，并用交叉验证的方法进行支持向量机参数选择。
In this paper, soft sensor modeling method based on Least Square SVM (LS SVM) is proposed, and cross validation method is used to select hyper-parameter of LS SVM model.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
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