首页|Neoma Business School Reports Findings in Machine Learning (Evaluating accession decisions in customs unions: a dynamic machine learning approach)

Neoma Business School Reports Findings in Machine Learning (Evaluating accession decisions in customs unions: a dynamic machine learning approach)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting from Rouen, France, by NewsRx journalists, research stated, “Previous work in the literature on regional econ omic integration has proposed the use of machine learning algorithms to evaluate the composition of customs unions, specifically, to estimate the degree to whic h customs unions match ‘natural markets’ arising from trade flow data or appear to be driven by other factors such as political considerations. This paper expan ds upon the static approaches used in previous studies to develop a dynamic fram ework that allows to evaluate not only the composition of customs unions at a gi ven point in time, but also changes in the composition over time resulting from accessions of new member states.”

RouenFranceEuropeCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.29)