# Restricted Boltzmann Machine with Multivalued Hidden Variables: a model suppressing over-fitting

arXiv: https://arxiv.org/abs/1811.12587 · HTML: https://mlsanity.com/paper/1811.12587/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Multi-layered Discriminative Restricted Boltzmann Machine with Untrained Probabilistic Layer](https://mlsanity.com/paper/2210.15434/) (2022)
- [Dataset-Free Weight-Initialization on Restricted Boltzmann Machine](https://mlsanity.com/paper/2409.07708/) (2024)

## Abstract (arXiv)

Generalization is one of the most important issues in machine learning problems. In this study, we consider generalization in restricted Boltzmann machines (RBMs). We propose an RBM with multivalued hidden variables, which is a simple extension of conventional RBMs. We demonstrate that the proposed model is better than the conventional model via numerical experiments for contrastive divergence learning with artificial data and a classification problem with MNIST.