Bayesian Probability Theory | a General Method for Machine Learning

This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or unsupervised. It can learn from different types of data, regardless of whether they are noisy or perfect, independent facts or behaviors of an unknown machine. These capabilities are (partially) demonstrated in the paper through the uniform application of the theory to two typical types of machine learning: in-cremental concept learning and unsupervised data classiication. The generality of the theory suggests that the process of learning may not have so many diierent \types" as currently held, and the method that is the oldest may be the best after all.