Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
Abstract. The aim of this paper is to study a family of logics that approximates classical inference, in which every step in the approximation can be decided in polynomial time. Fo...
Abstract. We introduce an automated multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We uti...
Abstract. In this paper, we discuss approximation spaces in a granular computing framework. Such approximation spaces generalise the approaches to concept approximation existing in...
We study properties of infomorphisms between information systems. In particular, we interpret infomorphisms between information systems in terms of sums with constraints (constrain...
The combination of fuzzy set and rough set theories lead to various models. Functional and set approaches are two categories based on different fuzzy representations. In this pape...
A parallel rule-extracting algorithm based on the improved discernibility matrix [2] is proposed, by this way, a large amount of raw data can be divided into some small portions to...