Learning Fast and Slow: A Unified Batch/Stream Framework

We propose an unified approach similar to the model proposed by Nobel Prize in Economics laureate Daniel Kahneman in his best-selling book “Thinking, Fast and Slow” to describe the mechanisms behind human decision-making. The central thesis of this book is a dichotomy between two modes of thought: System 1 is fast, instinctive and emotional; System2 is slower, more deliberative, and more logical.

In this paper, we present FAST AND SLOW LEARNING (FSL),  a novel unified framework that sheds light on the symbiosis between batch and stream learning. FSL works by employing Fast (stream) and Slow (batch) Learners, emulating the mechanisms used by humans to make decisions.

Jacob Montiel, Albert Bifet, Viktor Losing, Jesse Read, Talel Abdessalem: Learning Fast and Slow: A Unified Batch/Stream Framework. BigData 2018: 1065-1072

Paper at Research Gate

Jacob Montiel PhD Thesis: “Fast and slow machine learning”