# Weighted Multisource Tradaboost

## TL;DR

This paper introduces a weighted multisource Tradaboost method for transfer learning that considers the balance between source and target data, showing improved performance with increasing target samples.

## Contribution

It proposes a novel weighting scheme for Tradaboost that accounts for data availability, enhancing transfer learning effectiveness over existing methods.

## Key findings

- Weighted Tradaboost outperforms the base method with more target data
- Source-target ratio weighting improves transfer learning performance
- No-transfer SVM outperforms transfer methods with large target samples

## Abstract

In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data. This method builds on the state-of-the-art Multisource Tradaboost, but weighs the importance of each datapoint taking into account the amount of target and source data available. A comparative study is then presented exposing the performance of four transfer learning methods as well as the proposed Weighted Multisource Tradaboost. The experimental results show that the proposed method is able to outperform the base method as the number of target samples increase. These results are promising in the sense that source-target ratio weighing may be a path to improve current methods of transfer learning. However, against the asymptotic conjecture, all transfer learning methods tested in this work get outperformed by a no-transfer SVM for large number on target samples.