Aaron Bostrom

TL;DR: This work implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets, indicating that only nine of these algorithms are significantly more accurate than both benchmarks.

TL;DR: Through extensive experimentation on 72 datasets, it is demonstrated that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm.

TL;DR: The first iteration of the MTSC archive is formed, a collaborative effort between researchers at the University of East Anglia (UEA) and theUniversity of California, Riverside (UCR), which consists of 30 datasets with a wide range of cases, dimensions and series lengths.

TL;DR: Through extensive experimentation on 72 datasets, it is demonstrated that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm.

TL;DR: AirSurf is reported, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery.