首页|'Automated Dataset Reduction Based On Use Of Explainability Techniques' in Paten t Application Approval Process (USPTO 20240256955)
'Automated Dataset Reduction Based On Use Of Explainability Techniques' in Paten t Application Approval Process (USPTO 20240256955)
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The following quote was obtained by the news editors from the background informa tion supplied by theinventors: “Training machine learning models can be a time consuming process that involves a significantamount of manual review and multip le rounds of training. Further, training a machine learning modelmay require a large amount of training data some of which may not significantly contribute to improvingthe accuracy of the machine learning model. Some training data may eve n have a deleterious effect onthe performance of a machine learning model and e xcluding such data may be beneficial. However, theprocess of identifying traini ng data that impacts the pathways, nodes, and weighting of a machine learningmo del is difficult, in addition to being time consuming and resource intensive. Ac cordingly, there is a needto accurately reduce the size of a training dataset i n a manner that does not also reduce the accuracy ofthe machine learning model. ”
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NSTL
