Regularized classification for mixed continuous and categorical variables under across-location heteroscedasticity

Expert networks with mixed continuous and categorical feature variables: a location modeling approach

In the context of medically relevant artificial intelligence, many real-world problems involve both continuous and categorical feature variables. When the data are mixed mode, the assumption of multivariate Gaussian distributions for the gating network of normalized Gaussian (NG) expert networks, such as NG mixture of experts (NGME), becomes invalid. An independence model has been studied to ha...

Instrumental weighted variables under heteroscedasticity, Part I - Consistency

Multi-objective Facility Location-allocation Problem for Mixed Environment of Continuous and Discrete Random Fuzzy Variables

In this paper it is assumed that there are two types of customer demands. Demands of the first type are discrete fuzzy random variables with a poison distribution and the second are continuous fuzzy random variables with a normal distribution. In this problem the distribution centers (DC)s, are selected and allocated in a way that not only the total transportation cost of the problem is minimiz...

Surrogate Models for Mixed Discrete-Continuous Variables

Large-scale computational models have become common tools for analyzing complex manmade systems. However, when coupled with optimization or uncertainty quantification methods in order to conduct extensive model exploration and analysis, the computational expense quickly becomes intractable. Furthermore, these models may have both continuous and discrete parameters. One common approach to mitiga...

Selecting the Best Splits for Classification Trees With Categorical Variables