# PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach

arXiv: https://arxiv.org/abs/1606.07240 · HTML: https://mlsanity.com/paper/1606.07240/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

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

- [Learning via Wasserstein-Based High Probability Generalisation Bounds](https://mlsanity.com/paper/2306.04375/) (2023)
- [Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures](https://mlsanity.com/paper/2402.13285/) (2024)
- [PAC-Bayesian Domain Adaptation Bounds for Multi-view learning](https://mlsanity.com/paper/2401.01048/) (2024)
- [Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds](https://mlsanity.com/paper/2411.06276/) (2024)

## Abstract (arXiv)

We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the second level. Our main theoretical result is a generalization bound on the risk of the majority vote which exhibits a term of diversity in the predictions of the view-specific classifiers. From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. Finally, we experiment our principle on multiview datasets extracted from the Reuters RCV1/RCV2 collection.