We describe a dynamic programming algorithm that builds a decision rule for each row of a decision table. Rules are constructed according to the minimum description length principle: they have minimum length. Model complexity is regulated by adjusting rule accuracy.

A central question in the era of ’big data’ is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is...

Traditionally, Markov models have not been successfully used for compression of signal data other than binary image data. Due to the fact that exact substring matches in non-binary signal data are rare, using full resolution conditioning information generally tends to make Markov models learn slowly, yielding poor compression. However, as is shown in this paper, such models can be successfully ...

Determining the number of sources in a received wave field is a well-known and a well-investigated problem. In this problem, the number of sources impinging on an array of sensors is to be estimated. The common approach for solving this problem is to use an information theoretic criterion like the minimum description length (MDL) or the Akaike information criterion. Under the assumption that th...

This paper is concerned with an aspect of the structural properties of images of simple objects, namely their decomposition into generic parts using edge information only. In particular, it reports results of a psychological experiment conducted using experimentally naive subjects to assess their concept of part when presented with poor edge images. It compares the results with those of a recen...

This paper introduces online Bayesian network learning in detail. The structural and parametric learning abilities of the online Bayesian network learning are explored. The paper starts with revisiting the multi-agent self-organization problem and the proposed solution. Then, we explain the proposed Bayesian network learning, three scoring functions, namely LogLikelihood, Minimum description le...