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On the number of clusterings in a hierarchical classication model with overlapping clusters

Roman A., Podolak I. T., Deszyńska A.

Schedae Informaticae

2011

Vol. 20

137-157

This paper shows a new combinatorial problem which emerged from studies on an articial intelligence classication model of a hierarchical classier. We introduce the notion of proper clustering and show how to count their number in a special case when 3 clusters are allowed. An algorithm that generates all clusterings is given. We also show that the proposed approach can be generalized to any number of clusters, and can be automatized. Finally, we show the relationship between the problem of counting clusterings and the Dedekind problem.

A Neural Algorithm of Nonlinear Regression

Bielecki A., Podolak I. T., Bielecka M.

Schedae Informaticae

2004

Vol. 13

65-81

This article is devoted to the comparison between two alternative approaches to the economic models creation and to the estimation of their parameters. The first approach is to create a multiple regression model whereas the second one is to use neural modeling. Both methods are applied to estimation of an exemplary econometric model.

A neural system of phonematic transformation

Podolak I. T., Bielecki A.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2003

Vol. 7, No 1

115-13018

A common task in speech processing for which neural networks are widely employed is text-to-phoneme conversion. In this paper we propose a novel solution to this problem by combining a multilayer neural network and a modular hybrid system that uses basic rules to subdivide the original problem into easier tasks which are then solved by dedicated neural networks. A hybrid solution can be more rapidly constructed than a single net solution, and is easily extendable. Input data representation is also discussed. A voting committee concept is used to enhance generalization abilities of the system. Efficiency of the proposed systems is compared.