# Multi-Task Classification Hypothesis Space with Improved Generalization Bounds

arXiv: https://arxiv.org/abs/1312.2606 · HTML: https://mlsanity.com/paper/1312.2606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2013.

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

- [Collaborative Management for Chronic Diseases and Depression: A Double Heterogeneity-based Multi-Task Learning Method](https://mlsanity.com/paper/2511.16398/) (2025)

## Abstract (arXiv)

This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification. It extends similar hypothesis spaces that have previously considered in the literature. Assuming this space, an improved Empirical Rademacher Complexity-based generalization bound is derived. The analysis is itself extended to an MKL setting. The connection between the proposed hypothesis space and a Group-Lasso type regularizer is discussed. Finally, experimental results, with some SVM-based Multi-Task Learning problems, underline the quality of the derived bounds and validate the paper's analysis.