Extensive comparisons have shown that support vector machines obtain good generalization performance on various data sets.
大量的对比实验已经表明支持向量机在很多问题上获得了良好的推广能力。
Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.
集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。
The experiments show that the algorithm has higher generalization performance, and lower time and space complexity. It is a highly effective ensemble algorithm.
实验结果表明，该算法具有较高的泛化性能和较低的时、空复杂性，是一种高效的集成方法。
Compared with statistical theory, statistical learning theory focuses on the machine learning of small sample size and can trade off between the complexity of models and generalization performance.
与传统统计学相比，统计学习理论是一种专门研究小样本情况下机器学习规律的理论。
Because of the better generalization performance of SVM, less training samples are needed.
利用支撑矢量机具有更好的推广能力，可以使用较少的训练样本。
The results show that the model has the high steady-state precision and generalization performance.
结果表明，该模型有较高的稳态精度，而且具有较好的泛化能力。
In the paper, a method to improve the generalization performance of the Chinese text classifier is put forward.
本文提出了一种提高中文文本分类器推广性能的方法。
Diversity among base classifiers is known to be an important factor for improving generalization performance in ensemble learning.
差异性是提高分类器集成泛化性能的重要因素。
Parameter tuning of Support Vector Regression (SVR) has been a critical task to develop a SVR model with good generalization performance.
在回归支持向量机的建模中，参数调节问题一直是影响模型性能的重要因素之一。
This method greatly reduces the number of training examples and so improves the training speed of SVM, without the loss in generalization performance.
在保证支撑矢量机的分类能力情况下，该方法大大地减少了训练样本的个数，同时提高了支撑矢量机的训练速度。
Because of its strict mathematical theory of support and good generalization performance, it addresses the problem of small sample study of particular advantage.
由于它有严格的数学理论支撑以及较强的泛化性能，它在解决小样本学习问题时尤其具有优势。
First, this paper investigates the effect of initial weight ranges, learning rate, and regularization co-efficient on generalization performance and learning speed.
首先研究了初始权值的范围、学习率和正则项系数对泛化性能和学习速度的影响。
Simulation results show that the optimal selection approach based on PSO is available and the PSO-SVR model has superior learning accuracy and generalization performance.
仿真结果表明：该PSO优化SVR参数方法可行、有效，由此得到的SVR模型具有更好的学习精度和推广能力。
The wavelet neural network, which has good approximation and generalization performance in nonlinear modeling, is used to predict the final settlements of soft ground in expressway.
利用小波神经网络在非线性建模中的收敛迅速等优越性 ，提出利用小波神经网络预测高速公路软土地基的最终沉降量的方法。
The root mean square relative error, mean absolute relative error and maximize absolute relative error of SVM model generalization performance are 1.06%, 0.96% and 1.16%, respectively.
对SVM多元非线性回归泛化性能进行测试，其均方根相对误差为1.06%，平均绝对相对误差为0.96%，最大绝对相对误差为1.16%。
The main content in this paper include generalization performance research of CMAC NN and the application in Integral Variable Structure Control (IVSC) for uncertain nonlinear systems.
本文主要内容包括对CMAC神经网络泛化性能的研究及其在对不确定非线性系统进行积分变结构控制（IVSC）中的应用。
With real data collected from Jiangyin Xingcheng Steel Work CO. LTD, experiments show that SVM-based method is effective and superior to ANN-based method on generalization performance.
以江阴兴澄钢铁公司的实际数据进行实验，结果表明，支持向量机方法有着良好的泛化能力，优于人工神经网络建模方法。
According to the excellent generalization performance and global optimal property for peak of SVM, the method can suppress the random additive noise more effectively in contrast with traditional ones.
由于利用了SVM泛化能力强、全局最优等特点，因此该方法与传统方法相比，能更有效地抑制随机加性噪声。
According to the excellent generalization performance and global optimal property for peak of SVM, the method can suppress the random additive noise more effectively in contrast with traditional ones.
由于利用了SVM泛化能力强、全局最优等特点，因此该方法与传统方法相比，能更有效地抑制随机加性噪声。
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