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to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors
hope that this presentation of the theory and applications of Inductive Logic Programming will help
the reader understand the theoretical underpinnings of ILP, and also provide a helpful overview of the
state-of-the-art in the domain.
Chapter VIII by Kiruthika Ramanathan and Sheng Uei Guan presents a recursive approach to
unsupervised learning. The algorithm proposed, while similar to ensemble clustering, does not need
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is done between two subsets of data at one time, thereby saving training time. Also, only two kinds of
clustering algorithms are used in creating the recursive clustering ensemble, as opposed to the multi-
tude of clusterers required by ensemble clusterers. In this chapter, a recursive clusterer is proposed for
both single and multi-order neural networks. Empirical results show as much as 50% improvement in
clustering accuracy when compared to benchmark clustering algorithms.
Section IV discusses several applications of optimization problems. There are three chapters in this
section:
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(CaRBS) technique is based on Dempster-Shafer theory and, hence, operates in the presence of ignorance.
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of companies to being either failed or not-failed. Further results are found when an incomplete version
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to obtain optimal solution using satisfactory approach in uncertain environment. The optimal solution
is obtained by using possibilistic linear programming approach and intelligent computing by MATLAB.
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high quality and least cost products. The proposed fuzzy membership function allows the implementer
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an optimum way.
Chapter XI by Malcom Beynon investigates the modeling of the ability to improve the rank posi-
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nometric Differential Evolution, the minimum changes necessary to the criteria values of an alternative
are investigated for it to achieve an improved rank position. This investigation is compounded with a