改进随机初始化的极限学习机，让模型更稳定、更好用。
Effective Non-Random Extreme Learning Machine
- 用信号处理思想替代随机权重，直接设计隐藏层
- 在合成与真实数据上预测性能媲美传统ELM
- 适合需要稳定训练的回归任务使用者
极限学习机（ELM）是一种广泛应用于回归问题的统计技术。本质上，ELM是单层神经网络，其中隐藏层权重从特定分布随机采样，输出层权重则由数据学习得到。该方法面临两大挑战：一是隐藏层神经元数量的架构设计难题，二是对隐藏层权重随机初始化的敏感性。本文提出一种用于回归任务的新颖增强型学习算法——有效非随机极限学习机（ENR-ELM），简化了架构设计，无需随机选择隐藏层权重。所提方法将信号处理中的基函数与投影概念融入ELM框架，提出两种版本：近似版ENR-ELM和增量版ENR-ELM。在合成数据与真实数据上的实验结果表明，该方法克服了传统ELM的问题，同时保持了相当的预测性能。
原文摘要 · Abstract (English)
The Extreme Learning Machine (ELM) is a growing statistical technique widely applied to regression problems. In essence, ELMs are single-layer neural networks where the hidden layer weights are randomly sampled from a specific distribution, while the output layer weights are learned from the data. Two of the key challenges with this approach are the architecture design, specifically determining the optimal number of neurons in the hidden layer, and the method's sensitivity to the random initialization of hidden layer weights. This paper introduces a new and enhanced learning algorithm for regression tasks, the Effective Non-Random ELM (ENR-ELM), which simplifies the architecture design and eliminates the need for random hidden layer weight selection. The proposed method incorporates concepts from signal processing, such as basis functions and projections, into the ELM framework. We introduce two versions of the ENR-ELM: the approximated ENR-ELM and the incremental ENR-ELM. Experimental results on both synthetic and real datasets demonstrate that our method overcomes the problems of traditional ELM while maintaining comparable predictive performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。