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。

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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。

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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。

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