• Corpus ID: 235485579

Consistency of Extreme Learning Machines and Regression under Non-Stationarity and Dependence for ML-Enhanced Moving Objects

@inproceedings{Steland2020ConsistencyOE,
  title={Consistency of Extreme Learning Machines and Regression under Non-Stationarity and Dependence for ML-Enhanced Moving Objects},
  author={Ansgar Steland},
  year={2020}
}

• A. Steland
• Published 22 May 2020
• Computer Science

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 `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 `s-penalty are shown under weak conditions. The results also cover bounds for the sample squared predicition error.