# Information Theory and its Relation to Machine Learning

arXiv: https://arxiv.org/abs/1501.04309 · HTML: https://mlsanity.com/paper/1501.04309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2015.

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

- [Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning](https://mlsanity.com/paper/2305.14397/) (2023)

## Abstract (arXiv)

In this position paper, I first describe a new perspective on machine learning (ML) by four basic problems (or levels), namely, "What to learn?", "How to learn?", "What to evaluate?", and "What to adjust?". The paper stresses more on the first level of "What to learn?", or "Learning Target Selection". Towards this primary problem within the four levels, I briefly review the existing studies about the connection between information theoretical learning (ITL [1]) and machine learning. A theorem is given on the relation between the empirically-defined similarity measure and information measures. Finally, a conjecture is proposed for pursuing a unified mathematical interpretation to learning target selection.