Haroon Atique Babri

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It is well known that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons (including biases) can learn N distinct samples (x(i),t(i)) with zero error, and the weights connecting the input neurons and the hidden neurons can be chosen "almost" arbitrarily. However, these results have been obtained for the case when the(More)
Software systems need to evolve as business requirements, technology and environment change. As software is modified to accommodate the required changes, its structure deteriorates. There is increased deviation from the actual design and architecture. Very often, documentation is not updated to reflect these changes thus making it more and more difficult to(More)
Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main(More)
Multilayer perceptrons with hard-limiting (signum) activation functions can form complex decision regions. It is well known that a three-layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two-layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer(More)
Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature(More)
Software systems are expected to change over their lifetime in order to remain useful. Understanding a software system that has undergone changes is often difficult due to unavailability of up-to-date documentation. Under these circumstances, source code is the only reliable means of information regarding the system. In this paper, we apply data mining, or(More)
In this paper we describe a model for classifying binary data using classifiers based on Bernoulli mixture models. We show how Bernoulli mixtures can be used for feature extraction and dimensionality reduction of raw input data. The extracted features are then used for training a classifier for supervised labeling of individual sample points. We have(More)