For machine learning and AI to reach its full potential, other elements of supervised learning are often required to “boost” model performance and optimize results. Boosting is a term where weak models are made into strong models. Adaptive Boosting is a machine learning meta-algorithm that can be used in conjunction with many other types of learning algorithms to improve performance. In practice, this approach can be effectively applied to software testing. This session will focus on hybrid or ensemble approaches of machine learning that combine the inherent value of Unsupervised Learning, Supervised Learning, Reinforcement Learning and Computer Vision. Together, this combination outperforms stand-alone applications of machine learning or AI, delivering greater autonomy, speed, accuracy, and agility.
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