Non-convex cost functionals in boosting algorithms and methods for panel selection
Neural and Evolutionary Computing
2024-09-21 v1 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
In this document we propose a new improvement for boosting techniques as proposed in Friedman '99 by the use of non-convex cost functional. The idea is to introduce a correlation term to better deal with forecasting of additive time series. The problem is discussed in a theoretical way to prove the existence of minimizing sequence, and in a numerical way to propose a new "ArgMin" algorithm. The model has been used to perform the touristic presence forecast for the winter season 1999/2000 in Trentino (italian Alps).
Cite
@article{arxiv.cs/0102015,
title = {Non-convex cost functionals in boosting algorithms and methods for panel selection},
author = {Marco Visentin},
journal= {arXiv preprint arXiv:cs/0102015},
year = {2024}
}