# Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model

arXiv: https://arxiv.org/abs/2111.14427 · HTML: https://mlsanity.com/paper/2111.14427/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Distribution-Independent PAC Learning of Halfspaces with Massart Noise](https://mlsanity.com/paper/1906.10075/) (2019)
- [Efficiently Learning Adversarially Robust Halfspaces with Noise](https://mlsanity.com/paper/2005.07652/) (2020)
- [Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm](https://mlsanity.com/paper/1607.00567/) (2016)
- [RSSL: Semi-supervised Learning in R](https://mlsanity.com/paper/1612.07993/) (2016)

## Built on by (most cited first, 1 of 1)

- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)

## Abstract (arXiv)

We investigate the generalization properties of a self-training algorithm with halfspaces. The approach learns a list of halfspaces iteratively from labeled and unlabeled training data, in which each iteration consists of two steps: exploration and pruning. In the exploration phase, the halfspace is found sequentially by maximizing the unsigned-margin among unlabeled examples and then assigning pseudo-labels to those that have a distance higher than the current threshold. The pseudo-labeled examples are then added to the training set, and a new classifier is learned. This process is repeated until no more unlabeled examples remain for pseudo-labeling. In the pruning phase, pseudo-labeled samples that have a distance to the last halfspace greater than the associated unsigned-margin are then discarded. We prove that the misclassification error of the resulting sequence of classifiers is bounded and show that the resulting semi-supervised approach never degrades performance compared to the classifier learned using only the initial labeled training set. Experiments carried out on a variety of benchmarks demonstrate the efficiency of the proposed approach compared to state-of-the-art methods.