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。
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。
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。
The SVM method is based on seeking on the Structural Risk Minimization by few learning samples supporting, and it has important feature such as good generalization and classification performance, etc.
支持向量机方法基于小学习样本条件下，通过寻求结构风险最小，以期获得良好的分类效果和泛化能力。
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。
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。
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。
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