Abstract
As we have previously seen, the general purpose of machine learning is to automate the process of acquiring knowledge based on observed data. Despite this general objective, the way in which this process is carried out may vary considerably depending on the nature of considered data and the final goal of learning. Thus, it can be characterized in many completely different ways. What remains unchanged, however, is that the nature of the learning paradigms. Despite their apparent variety, they can be roughly called “statistical”, which means that they are all related to revealing the statistical nature of the underlying phenomenon. To this end, the main goal of statistical learning theory is to analyze and formalize the process of knowledge acquisition and to provide a theoretical basis and an appropriate context for its analysis within a statistical framework.
Published Version
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