Learning classification rules using Bayes

TL;DR: In this paper, the problem of theory refinement under uncertainty is reviewed in the context of Bayesian statistics, a theory of belief revision, reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and can be incrementally refined from data.

TL;DR: A set of data set descriptors is developed to help decide which algorithms are suited to particular data sets, including data sets with extreme distributions and with many binary/categorical attributes.

TL;DR: New results are presented which show that within a Bayesian framework not only grammars, but also logic programs are learnable with arbitrarily low expected error from positive examples only and the upper bound for expected error of a learner which maximises the Bayes' posterior probability is within a small additive term of one which does the same from a mixture of positive and negative examples.

TL;DR: In an extensive empirical study, RISE consistently achieves higher accuracies than state-of-the-art representatives of both its parent approaches, as well as a decision tree learner (C4.5).

TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.

TL;DR: An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.

TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.