[2407.12288] Information-Theoretic Foundations for Machine Learning

View the PDF file for the paper entitled The Foundations of theoretical Information for Automated Learning, by Hong John Jeon and other authors

PDF view

a summary:The progress of machine learning cannot be denied over the past decade. In the past, it is great and worrying that this progress was to be investigated with a slim or non -existent theory to direct the experiment. Despite this fact, practitioners were able to direct their future experience through observations from the previous extensive experimental investigations. In this work, we suggest a theoretical framework that tries to provide rigor for current practices in machine learning. As for the theory, we present a strict sports framework and leaves many interesting ideas for exploration in the future. For the practitioner, we offer a framework that is simple results, and we provide intuition to direct future investigations through a wide range of learning models. Tangantly, we offer a theoretical framework rooted in the statistics of Baysi and the theory of Shannon information, which is general enough to unify the analysis of many phenomena in machine learning. Our framework describes the optimal Baysi educated performance because he learns from a set of experience. Unlike current analyzes that weaken with increased complexity of data, our theoretical tools provide accurate visions through various automatic learning settings. During this work, we derive theoretical results and appear its vertical by applying them to derive visions of the settings. These settings range from learning from data that are distributed independently and similar under an unknown distribution, to serial data, to data that shows a learning hierarchical structure, and finally to data that cannot be fully interpreted under the learner’s beliefs (miscondception). These results are particularly relevant because we strive to understand and overcome the increasingly difficult machine learning challenges in this complex world indefinitely.