Abstract
New research on artificial intelligence is the subject of a new report. According to news reporting out of Paris, France, by NewsRx editors, research stated, “Machine learning approaches are currently used to understand or model complex physical systems.” The news correspondents obtained a quote from the research from Arts et Metiers Institute of Technology: “In general, a substantial number of samples must be collected to create a model with reliable results. However, collecting numerous data is often relatively time-consuming or expensive. Moreover, the problems of industrial interest tend to be more and more complex, and depend on a high number of parameters. High-dimensional problems intrinsically involve the need for large amounts of data through the curse of dimensionality. That is why new approaches based on smart sampling techniques have been investigated to minimize the number of samples to be given to train the model, such as active learning methods. Here, we propose a technique based on a combination of the Fisher information matrix and sparse proper generalized decomposition that enables the definition of a new active learning informativeness criterion in high dimensions.”
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