ABSTRACT Extreme learning machine for regression (ELR), though efficient, is not preferred in time-limited applications, due to the model selection time being large. To overcome this problem, we reformulate ELR to take a new regularization parameter nu (nu-ELR) which is inspired by Schölkopf et al. The regularization in terms of nu is bounded between 0 and 1, and is easier to interpret compared to C. In this paper, we propose using the active set algorithm to solve the quadratic programming optimization problem of nu-ELR. Experimental results on real regression problems show that nu-ELR performs better than ELM, ELR, and nu-SVR, and is computationally efficient compared to other iterative learning models. Additionally, the model selection time of nu-ELR can be significantly shortened.

中文翻译：

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nu正则化的极限学习回归

摘要 用于回归的极限学习机（ELR）虽然高效，但在时间有限的应用中并不是首选，因为模型选择时间很大。为了克服这个问题，我们重新制定 ELR 以采用新的正则化参数 nu (nu-ELR)，其灵感来自 Schölkopf 等人。nu 方面的正则化在 0 和 1 之间有界，与 C 相比更容易解释。在本文中，我们提出使用活动集算法来解决 nu-ELR 的二次规划优化问题。实际回归问题的实验结果表明，nu-ELR 的性能优于 ELM、ELR 和 nu-SVR，并且与其他迭代学习模型相比计算效率更高。此外，nu-ELR 的模型选择时间可以显着缩短。