Dear Dr. Fordyce,
Dear Ken,
Thank you very much again for this interesting, deep, and at the same time fresh impulse.
Perhaps I can make a few additional comments.
In fact, it always seems to me to be a good practice to view machine learning against the background of statistics,
including optimal experimental design, and thus also mathematics in general. Machine learning was incorporated into
statistical learning at a fairly early stage.
I would like to mention the mathematical optimization theory foundations of machine learning via support vector machines.
Furthermore, even today, mathematical modeling is often discussed when discussing statistics or machine learning.
Statistical learning, like deep learning, interacted early on, even at the level of their motivations and inspirations,
with brain research and other neurosciences, including cognitive science, so that the connection to artificial
intelligence emerged early on.
Over the years, some engineering disciplines have often referred to interfaces with all these fields as inverse problems.
These include more traditional tomography and discrete tomography, image generation and image processing, the same with
video and sound, and, concerning all our senses, remote sensing, and the study of gravitational anomalies, which today
leads us to earth sciences, space sciences, and cosmology.
Early-Warning Systems and what I call Early-Chance Indicator Systems demonstrate that this does not exclude our OR-MS
domain, but rather affects it and presents opportunities for it. These systems are used in all areas of our lives,
including economics and business.
Increasingly, hybrid systems, gene networks, metabolic networks, gene-environment networks, eco-finance networks, and
regulatory systems, as well as stochastic calculus, are also helping here, and recently also with regime switching and
paradigm shifting.
As some of the above terms indicate, the entire field of machine learning and modeling is closely and mutually linked to
optimal decision making.
Not only machine learning, but OR-MS as a whole, including analytics, has enormous potential to make a unique contribution
and serve sustainably in the future.
Many thanks to you again,
and to the whole INFORMS team, which faithfully provides this excellent forum for fruitful exchange.
With kind regards,
best wishes,
Willi
© Gerhard-Wilhelm Weber, "Times and Lives" (in completion)
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Gerhard-Wilhelm Weber
Professor
Poznan University of Technology
Poznan
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Original Message:
Sent: 03-23-2025 13:23
From: kenneth fordyce
Subject: Machine Learning compared to computational probability and statistics from the 1970s and early 1980s
As person from the "before times", I appreciate some elements of current machine learning. Some of it strikes as material that has been known from the before times. For example the terms one hot encoding versus Label Encoding. These are essentially methods to make qualitative (or categorical) data into quantitative. They are presented as new without recognition they have been around since the before times, trivial to do on programming environments such as APL (Array/Analytics programming language) . AND without reference to burst of work done in the statistics community in categorical and ordinal categorical analysis - for example work by Agresti. One hot encoding is a mainstay of binary formulations in MILP. Assigning integers a mainstay of ordinal data analysis.
Question - is something lost to the greater analytics community.
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kenneth fordyce
director analytics without borders
Arkieva
Wilmington DE
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