Machine Learning Algorithms and Development Research
Abstract
This article examines machine learning's fundamental taxonomy, which includes supervised learning, unsupervised learning, and reinforcement learning. It includes study of basic machine learning techniques such as the decision tree algorithm, the random forest algorithm, the artificial neural network algorithm, the SVM algorithm, the Boosting and Bagging algorithm, and the BP algorithm. The goal is to increase people's understanding of machine learning and expedite its popularisation by developing theoretical systems, further improving autonomous learning capabilities, integrating numerous digital technologies, and promoting personalised bespoke services.
References
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