# Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

arXiv: https://arxiv.org/abs/1808.05784 · HTML: https://mlsanity.com/paper/1808.05784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [A PAC-Bayes Analysis of Adversarial Robustness](https://mlsanity.com/paper/2102.11069/) (2021)
- [Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds](https://mlsanity.com/paper/2411.06276/) (2024)
- [Gradient-Boosted Based Structured and Unstructured Learning](https://mlsanity.com/paper/2302.14299/) (2023)
- [Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning](https://mlsanity.com/paper/2409.13791/) (2024)

## Abstract (arXiv)

In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.