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Abstract: To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to constructing a neural tree to integrate the advantages of decision trees and neural networks. The proposed neural tree, called a quadratic-neuron-based neural tree (QUANT), is a tree-structured neural network composed of neurons with quadratic neural-type junctions for pattern classification. A quadratic neuron is capable of forming a hyper-ellipsoid that can be varied in sizes and in locations on the space spanned by the input variables. Via a batch-mode training algorithm, the QUANT grows a neural tree containing quadratic neurons in its nodes. These quadratic neurons recursively partition the feature space into hyper-ellipsoidal-shaped sub-regions. The QUANT has the partial incremental capability so that it does not need to re-construct a new neural tree to accommodate new training data whenever new data are introduced to a trained QUANT. To demonstrate the performance of the proposed QUANT, one pattern recognition problem and the spam e-mail detection problem were tested. [ABSTRACT FROM AUTHOR]

Abstract: Prevention of financial risk is one of the major tasks that construction companies have to pay attention to. Using derivatives to avoid such risks is a practical strategy, but is heavily dependent on the traders’ skills and accuracy of predictions. The purpose of this study is to develop an automatic expert model using a rule extraction based approach that provides practitioners with a prediction tool for the hedging of financial risks through the use of derivatives. Data for the study include 780 quarterly financial statements collected from 2002 to 2006, based on public information from 39 listed construction companies in Taiwan. Statements with incomplete and missing data are eliminated, leaving 672 with which to construct the rule extraction based model, the Hyper Rectangular Composite Neural Networks (HRCNNs). After factor dimension reduction, only 16 financial ratios out of all revealed ratios are left to be used as input variables. The HRCNNs yield an 80.6% successful classification rate. With these 16 financial ratios and the proposed model, derivative use to hedge financial risk can be established for the benefit of the construction practitioners. [Copyright &y& Elsevier]