Machine Learning as Ecology
This work provides a novel ecological perspective for statistical learning, potentially enabling the design of ecosystems for machine learning, but it appears incremental as it applies an existing framework to a known method.
The paper tackles the interpretation of machine learning algorithms by showing that Support Vector Machines (SVMs) can be viewed through ecological dynamics, and it results in new online SVM algorithms that are benchmarked on the MNIST dataset.
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.