We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The of logistic inception is formulated as support vector regression problem. Using quantum feature map, the data are mapped to states belonging space, where associated kernel determined by inner product between states. This can be efficiently estimated on computer. conduct e...

Abstract Support vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that can describe the dynamic characteristics of financial processes make more accurate forecasts than other machine learning techniques. The first main contribution this paper to propose methodology modelling forecasting...

Most works on Support Vector Regression (SVR) focus kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance datasets. However, fixed radius limitation prevents adaptive selection of according to data distribution characteristics, compromising SVR-based methods. Therefore, this study proposes a...

This paper studies the addition of linear constraints to Support Vector Regression when kernel is linear. Adding those into problem allows add prior knowledge on estimator obtained, such as finding positive vector, probability vector or monotone data. We prove that related optimization stays a semi-definite quadratic problem. also propose generalization Sequential Minimal Optimization algorithm...