I am very proud to have had the honour of supervising Jiangning Song. His exceptional PhD has just been recognised with the 2019 Mollie Holman Medal.
Our enhanced lower bound for Dynamic Time Warping is tighter than the widely used LB Keogh without any additional computational burden. This picture shows Bart Goethals as he realises the impact it is bound to have. For more details see our SDM-19 paper here.
Thank you @AusDMConf and @CharlesStuartUniversity for the opportunity to present my group’s work on Concept Drift http://i.giwebb.com/research/concept-drift/. Thanks also for the lovely CSU wines. @MonashUni @@MonashInfoTech
We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree–-"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree–-obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.
We are looking for a leading researcher to lead a group of data science faculty. This is a bit like a Departmental Chair position.
The Living with AI discussion series will feature conversations about social and ethical implications of artificial intelligence. Join us for the first of these conversations, featuring AI expert Terry Cailli addressing the question "Can Computers Behave Ethically?"
Date and Time
Wed. 8 August 2018
5:30 pm – 7:00 pm AEST
Collins Square, Tower 2
Level 12, 727 Collins Street
Docklands, VIC 3008
We are very honoured to have received the 2018 SIAM International Conference on Data Mining (SDM-18) Best Research Paper Award for
Efficient search of the best warping window for Dynamic Time Warping.
Tan, C. W., Herrmann, M., Forestier, G., Webb, G. I., & Petitjean, F.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018.
This paper presents algorithms that allow a critical hyper-parameter for time series classification, the warping window, to be efficiently tuned.
The paper can be downladed here: i.giwebb.com/wp-content/papercite-data/pdf/TanEtAl18.pdf.
The code can be downloaded here: https://t.co/acY3A3SYyQ.
We have three continuing (Australia's version of tenured) faculty positions in data science. Join a strong data science group in a top-100 university in the world's most livable city. Closing date 19 November 2017. More details including application process here.
Fantastic planning session at the EODC. Amazing problems for time series analysis, starting with how to scale the state-of-the-art from being challenged by datasets containing 10 thousand series to routinely handling 10 trillion. Improved analytics for earth observation data have massive expected benefits for environmental monitoring, agriculture and economic planning, to name but a few. As always, it is both scary and exciting to be at the start of a massive undertaking.
I am excited to be joining the extraordinary team at Froomle as a Technical Advisor and looking forward to helping refine our best of class recommendation engine.
Why not join us at the Fourth International Conference on Information Retrieval and Knowledge Management to be held in Kota Kinabalu, Malaysia from 26 – 28 March 2018: http://camp18.pecamp.org/?