The broad learning system (BLS) is an emerging flat network, which has demonstrated its outstanding performance in classification and regression problems. The regularization plays an important role in the performance of the BLS. In real applications, since the BLS network is usually expanded dynamically, a predetermined regularization parameter may reduce the performance of the network. Using a fixed regularization in some cases, the classification accuracy of the BLS decreases dramatically when we expand the network. To alleviate this problem, we propose a method that automatically finds appropriate regularization parameters for different datasets, which is based on the weighted generalized cross-validation (WGCV). The experimental results indicate that the WGCV method improves the performance of the BLS, and alleviates the accuracy decrease of the incremental learning algorithm.

中文翻译：

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广义学习系统的加权广义交叉验证正则化

广义学习系统（BLS）是一种新兴的平面网络，在分类和回归问题上表现出卓越的性能。正则化在 BLS 的性能中起着重要作用。在实际应用中，由于BLS网络通常是动态扩展的，预定的正则化参数可能会降低网络的性能。在某些情况下使用固定的正则化，当我们扩展网络时，BLS 的分类精度会急剧下降。为了缓解这个问题，我们提出了一种基于加权广义交叉验证（WGCV）自动为不同数据集找到适当正则化参数的方法。实验结果表明，WGCV方法提高了BLS的性能，