Artificial Intelligence For Big Data Streams
I was pleased to be invited to give a Keynote talk at The Ninth International Conference On Big Data Analytics
Continue readingChallenges of Machine Learning for Data Streams in the Banking Industry
I was honoured that Björn Bringmann, Deloitte AI Institute Managing Director, used one of my quotes for the Advent Calendar
Continue readingDeloitte AI Institute – AI Quotes/Facts Advent Calendar
This whitepaper discusses current AI capabilities in Aotearoa New Zealand and offers recommendations for establishing Aotearoa New Zealand as a
Continue readingWHITE PAPER: Aotearoa New Zealand Artificial Intelligence A Strategic Approach
River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River’s ambition is to
Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given
Continue readingMachine learning for streaming data: state of the art, challenges, and opportunities
The Streaming Random Patches (SRP) algorithm is a new ensemble method specially adapted to stream classification which combines random subspaces
We propose an unified approach similar to the model proposed by Nobel Prize in Economics laureate Daniel Kahneman in his
Continue readingLearning Fast and Slow: A Unified Batch/Stream Framework
Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at
Continue readingMachine Learning for Data Streams: with Practical Examples in MOA
Rainer Maria Rilke gave in “Letters to a Young Poet” some advice to a young poet on how a poet
In this paper, we discuss the surprising result that non-change detectors can outperform change-detectors when used in a classification streaming
Continue readingClassifier Concept Drift Detection and the Illusion of Progress