Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that adding small number stages -- between 2--5 to symbolic regressor, statistically significant improvements can often be attained. We note coding SyRBo on top any regressor is straightforward, and...

Modern-day problems in statistics often face the challenge of exploring and analyzing complex non-Euclidean object data that do not conform to vector space structures or operations. Examples such objects include covariance matrices, graph Laplacians networks, univariate probability distribution functions. In current contribution a new concurrent regression model is proposed characterize time-va...