# Machine Learning as Ecology

## TL;DR

This paper introduces a novel ecological perspective on machine learning algorithms, especially SVMs, leading to new online methods and insights into their dynamics, with empirical validation on the MNIST dataset.

## Contribution

It provides a new ecological interpretation of SVMs and develops ecological-inspired online algorithms for machine learning.

## Key findings

- New ecological interpretation of SVMs
- Development of ecological-inspired online SVM algorithms
- Benchmark performance on MNIST dataset

## Abstract

Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning. Supplemental code is found at https://github.com/owenhowell20/EcoSVM.