Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.
现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。
Tracking random targets with Support Vector Regression (SVR) is studied and compared with the Least Square (LS) estimate in this paper.
本文研究了支持向量回归(SVR)在机动目标跟踪中的应用，并与传统回归方法最小二乘法(LS)进行了比较。
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。
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。
应用推荐