提出KSCN模型，提升非线性回归的表达能力与稳定性。
Kernel Stochastic Configuration Networks for Nonlinear Regression
- 用随机基构造再生核希尔伯特空间，实现核化SCN。
- 在3个基准数据集上表现优于原始SCN和典型核方法。
- 对核参数不敏感，适合工业场景中的稳定回归任务。
随机配置网络（SCNs）是一类随机参数分配的生成式学习模型，具有算法层面的通用逼近性质。本文提出一种核化版本的SCN，称为KSCN，旨在增强模型的表示学习能力与性能稳定性。构建的SCN模型所使用的随机基可张成再生核希尔伯特空间（RKHS），并基于此提出构建KSCN的算法。结果表明，重构空间中的数据分布有利于回归求解，且所提出的KSCN学习器具有通用逼近性质。研究使用包含两个工业数据集在内的三个基准数据集进行性能评估。实验结果表明，与现有方法相比，所提出的KSCN在学习性能、模型稳定性及对核参数设置的鲁棒性方面显著优于原始SCN和一些典型核方法。
原文摘要 · Abstract (English)
Stochastic configuration networks (SCNs), as a class of randomized learner models, are featured by its way of random parameters assignment in the light of a supervisory mechanism, resulting in the universal approximation property at algorithmic level. This paper presents a kernel version of SCNs, termed KSCNs, aiming to enhance model's representation learning capability and performance stability. The random bases of a built SCN model can be used to span a reproducing kernel Hilbert space (RKHS), followed by our proposed algorithm for constructing KSCNs. It is shown that the data distribution in the reconstructive space is favorable for regression solving and the proposed KSCN learner models hold the universal approximation property. Three benchmark datasets including two industrial datasets are used in this study for performance evaluation. Experimental results with comparisons against existing solutions clearly demonstrate that the proposed KSCN remarkably outperforms the original SCNs and some typical kernel methods for resolving nonlinear regression problems in terms of the learning performance, the model's stability and robustness with respect to the kernel parameter settings.
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