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。
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。
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。
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。
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。
The algorithm improves the generalization performance of feedforward neural networks through combining the regularization and pruning technology.
正则最小二乘算法将正则化网络和节点删除算法结合起来，大大提高了前馈网络的泛化性能。
As reserving typical samples and reducing training samples, the generalization performance and training efficient of the classifier are guaranteed.
即保留了典型样本，减少了训练样本数量，从而保证分类器的性能并且训练效率较高。
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。
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。
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。
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。
Simulation results for both artificial and real data show the generalization performance of our method is a good approximation of SVMs and the computation complexity is largely reduced by our method.
文中最后对人工和实际样本进行了实验，结果说明了线性规划支撑矢量机在推广能力上较好地逼近了原支撑矢量机，而在计算复杂度上明显低于原支撑矢量机。
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。
Generalization ability of ANN's identification model concerns with many factors, and appropriate designed performance index function is an important influence factor.
大量研究表明，ANN模型泛化能力的改善与很多因素相关联，其中恰当的性能指标函数设计是一个重要影响因素。
SVM has been applied to many fields such as pattern recognition, data mining, modeling and control of nonlinear system due to good generalization ability and globally optimal performance.
SVM由于其良好的泛化能力和全局最优性能，在模式识别、数据挖掘、非线性系统建模和控制等领域中展现出广泛的应用前景。
However, It can be said that the work on P2P media streaming systems is still in the early stage, and there is still some room for improvement of performance and generalization of model.
然而，可以说P 2 P流媒体系统方面的研究仍处于初期，其性能改善和模型一般化仍然有一些研究余地。
The results of test show that the presented classifier has good performance on memory and generalization. The accuracy can be compared with many traditional methods.
实验结果表明，所提出的新型分类器具有良好的记忆和泛化性能，准确率可以与许多传统方法相比较。
Simulation results for artificial data show that the proposed method gives good performance on reducing the effects of noise and improves the regression accuracy and generalization.
仿真实验结果显示了该方法有效地减少了噪声的影响，改进了回归的精度，增强了推广能力。
The two algorithms have excellent anti-noise performance, and the prediction model has strong ability of generalization.
两种算法具有很强的抗噪声能力，预测模型也具有很好的推广性。
Experiment results show its better performance in the efficiency of detecting system and generalization ability.
实验表明，算法在检测系统效率和推广性方面有较好的表现。
Support Vector machine is a new machine-learning method and has its unique advantages in pattern recognition because of outstanding learning performance and good capabilities in generalization.
支持向量机是一种全新的机器学习方法，其出色的学习性能和泛化能力强等方面的优势，在模式识别领域中有其独到的优越性。
Support Vector machine is a new machine-learning method and has its unique advantages in pattern recognition because of outstanding learning performance and good capabilities in generalization.
支持向量机是一种全新的机器学习方法，其出色的学习性能和泛化能力强等方面的优势，在模式识别领域中有其独到的优越性。
应用推荐