The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

Interpretable support vector regression

This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS). It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models. However, the transformed support vector model does not automatically result in an interpretable fuzzy model. Training of a support vector model results a complex rule...

Tree Structured Interpretable Regression

Functional Linear Regression That’s Interpretable

Regression models to relate a scalar Y to a functional predictor X(t) are becoming increasingly common. Work in this area has concentrated on estimating a coefficient function, β(t), with Y related to X(t) through ∫ β(t)X(t) dt . Regions where β(t) = 0 correspond to places where there is a relationship between X(t) and Y . Alternatively, points where β(t)= 0 indicate no relationship. Hence, for...

GPS Multipath Mitigation: A Nonlinear Regression Approach

Under assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression problem. We present a method for estimating code multipath error of GPS ground fixed stations. By formula...

Nonlinear functional regression: a functional RKHS approach

This paper deals with functional regression, in which the input attributes as well as the response are functions. To deal with this problem, we develop a functional reproducing kernel Hilbert space approach; here, a kernel is an operator acting on a function and yielding a function. We demonstrate basic properties of these functional RKHS, as well as a representer theorem for this setting; we i...