… Jump models switch infrequently between states to fit a sequence of data while taking the …
for joint feature selection, parameter and state-sequence estimation in jump models. …

… so-called jump models. In the final part of this section we will discuss how feature selection
… can be used to do feature selection in the previously mentioned jump model framework. …

… However, real-world applications often pose challenges for HMMs, due to model misspecification,
lack of data, feature selection issues, and computational challenges. It is well-known …

… to specific functions and ignore the noise caused by the irrelevant features. In this case…
feature selection framework to extract the function-relevant features might help improve the model …

… of regime identification and feature selection. In the following work, we propose a new set of
models called Regularised Jump Models that are founded upon the Jump Model framework. …

… jump model, a recently developed, interpretable and robust regime-switching model, to infer
key features … Using the data-driven feature selection methodology, we are able to determine …

… The model for calculating the pheromone ratio of jumping spiders is as follows: … jumping
spider individual is evaluated, and the best jumping spider is retained. The feature selection …

… suitable information criteria for hyperparameters selection. In extensive simulation … features
that drive the return dynamics of the world equity market. We find that a three-state model …

… to model and recognize human activities. In this paper, we focus on feature selection and …
This observation can be explained by the fact that normally people can not jump straight up …

Feature selection (FS) is a crucial data pre-processing process in classification problems. It
aims to reduce the dimensionality of the problem by eliminating irrelevant or redundant …