WebDetecting feature interactions is an important post-hoc method to explain black-box models. The literature on feature interactions mainly focus on det… Web21 de set. de 2024 · Here we describe our classification methodology based on the High Dimensional Model Representation (HDMR) expansion, studied in detail in [10–13].We first briefly review the general theory, how it applies to a binary classification problem, and how the categorical observations simply the process.

Theorems and examples on high dimensional model representation

WebHigh-Dimensional Model Representation, Sparse Grids, High-Performance Com-puting, International Real Business Cycles AMS subject classi cations. 41A63, 41A58, 68W25, 68W10, 91B70 1. Introduction. ... The HDMR technique has been applied to multiple elds including chem- Web22 de set. de 2016 · High dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for capturing high dimensional … interpretable attention mechanism

A surrogate model for computational ... - Wiley Online Library

WebHigh Dimensional Model Representation. The approach, known as Gradient En-hanced Kriging based High Dimensional Model Representation, is accompanied by a highly e cient sequential sampling scheme LOLA-Voronoi and is applied to various high dimensional benchmark functions and one real-life simulation problem of varying … WebHigh dimensional model representations (HDMR) are a particular family of representations where each term in the representation reflects the individual or … WebAbstractA family of multivariate representations is introduced to capture the input–output relationships of high‐dimensional physical systems with many input variables. A systematic mapping procedure between the inputs and outputs is prescribed to reveal the hierarchy of correlations amongst the input variables. It is argued that for most well‐defined physical … interpretable ai book