Outputs (12)

Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies (2017)
Presentation / Conference Contribution
Olorisade, B. K., Brereton, P., & Andras, P. (2017). Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies. In EASE'17: Proceedings of the 21st International Conference on Evaluation and Assessment in Softwa

Background:: Statistical validity and model complexity are both important concepts to enhanced understanding and correctness assessment of computational models. However, information about these are often missing from publications applying machine lea... Read More about Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies.

High-dimensional function approximation with neural networks for large volumes of data (2017)
Journal Article
Andras, P. (2018). High-dimensional function approximation with neural networks for large volumes of data. IEEE Transactions on Neural Networks and Learning Systems, 29(2), 500-508. https://doi.org/10.1109/TNNLS.2017.2651985

Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation... Read More about High-dimensional function approximation with neural networks for large volumes of data.