论文标题
极端学习机的一致性和在非平稳性和对ML增强对象的依赖性下的回归
Consistency of Extreme Learning Machines and Regression under Non-Stationarity and Dependence for ML-Enhanced Moving Objects
论文作者
论文摘要
通过极限学习机的监督学习。在非平稳的空间抽样设计下研究了具有随机权重的神经网络，该设计特别解决了在非平稳空间环境中移动的自主对象收集和分析数据的设置。随机模型特别允许空间异质性和弱依赖性。研究了效率和计算廉价的学习方法（不受约束的）最小二乘，脊回归和$ \ ell_s $ penalizatizatizanized最小二乘（包括拉索）。在弱条件下显示了最小二乘和脊回归估计值的一致性和渐近正态性，以及$ \ ell_s $ - penalty的相应一致性结果。结果还涵盖了样品平方倾向误差的边界。
Supervised learning by extreme learning machines resp. neural networks with random weights is studied under a non-stationary spatial-temporal sampling design which especially addresses settings where an autonomous object moving in a non-stationary spatial environment collects and analyzes data. The stochastic model especially allows for spatial heterogeneity and weak dependence. As efficient and computationally cheap learning methods (unconstrained) least squares, ridge regression and $\ell_s$-penalized least squares (including the LASSO) are studied. Consistency and asymptotic normality of the least squares and ridge regression estimates as well as corresponding consistency results for the $\ell_s$-penalty are shown under weak conditions. The results also cover bounds for the sample squared predicition error.
