Information granulation and concept approximation are some of the fundamental issues of granular computing. Granulation of a universe involves grouping of similar elements into granules to form coarse-grained views of the universe. Approximation of concepts, represented by subsets of the universe, deals with the descriptions of concepts using granules. In the context of rough set theory, this p...

In this paper, we We a close look at the problem of l&ing simple neural concepts under the uniform diseibution of examples By simple neural concepts we mean concepts that can be represented as simple combinations of perceptrons (halfspaces). One such class of concepts is the class of halfspace intersections. By formalizing the problem of learning halfspace in&ections as a sei-coveringproblem. w...

Covering based Rough Sets are an important generalization of Rough Set Theory. Basically, they replace the partition generated from an equivalence relation by a covering. In this context many approximation operators can be defined [16, 26, 27, 28, 34]. In this paper we want to discover relationships among approximation operators defined from neighborhoods. We use the concepts of duality, adjoin...

This paper investigates the concept approximation problem using ontology as an domain knowledge representation model and rough set theory. In [7] [8], we have presented a rough set based multi-layered learning framework for approximation of complex concepts assuming the existence of a simple concept hierarchy. The proposed methodology utilizes the ontology structure to learn compound concepts u...

Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concepts from experimental data using inference diagrams based on domain knowledge. Our solution is based on the rough set and rough mereological approaches. The effectiveness of the proposed approach is performed and evaluated on art...

Bring home now the book enPDFd transitioning to open systems concepts methods and architecture to be your sources when going to read. It can be your new collection to not only display in your racks but also be the one that can help you fining the best sources. As in common, book is the window to get in the world and you can open the world easily. These wise words are really familiar with you, i...

In this paper we introduce a normal form for ALC concepts and TBoxes called linkless normal form. We investigate properties of concepts given in this normal form such as an efficient satisfiability test and the calculation of uniform interpolants. We further show a way to approximate a TBox by a concept in linkless normal form, which allows us to check certain subsumptions efficiently. This mak...

We consider online prediction problems of combinatorial concepts. Examples of such concepts include s-t paths, permutations, truth assignments, set covers, and so on. The goal of the online prediction algorithm is to compete with the best fixed combinatorial concept in hindsight. A generic approach to this problem is to design an online prediction algorithm using the corresponding offline (appr...