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Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

DGM: A deep learning algorithm for solving partial differential equations

An activation function adapting training algorithm for sigmoidal feedforward networks

It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG) Repository Data (release 9) for the experiment, this paper examines the performance of back propagation feed forward neural network to estimate the Source Lines of Code. Multiple training algorithms are used in the experiments. Results demonstrate that the neural network models trained using Bayesian Regularization provide the best results and are suitable for this purpose.

Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points

Software maintenance is a task that every development group has to face when the software is delivered to the customers' site, installed and is operational. The time spent and effort required for keeping software operational consumes about 40-70% of cost of entire life cycle. This study proposes a four parameter integrated measure of software maintainability using a fuzzy model. The study also includes empirical data of maintenance time of projects which has been used to validate the proposed model.

Measurement of Software Maintainability Using a Fuzzy Model

Sigmoidal feedforward artificial neural networks (FFANNs) have been established to be universal approximators of continuous functions. The universal approximation results are summarized to identify the function sets represented by the sigmoidal FFANNs with the universal approximation properties. The equicontinuous properties of the identified sets is analyzed. The equicontinuous property is related to the fault tolerance of the sigmoidal FFANNs. The generally used arbitrary weight sigmoidal FFANNs are shown to be nonequicontinuous sets. A class of bounded weight sigmoidal FFANNs is established to be equicontinuous. The fault-tolerance behavior of the networks is analyzed and error bounds for the induced errors established.

Feedforward sigmoidal networks - equicontinuity and fault-tolerance properties

In conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training process. In this paper, we review constructive neural network algorithms that constructing feedforward architecture for regression problems. Cascade-Correlation algorithm (CCA) is a well-known and widely used constructive algorithm. Cascade 2 algorithm is a variant of CCA that is found to be more suitable for regression problems and is reviewed in this paper. We review our recently proposed two constructive algorithms that emphasize on architectural adaptation and functional adaptation during training. To achieve functional adaptation, the slope of the sigmoidal function is adapted during learning. The algorithm determines not only the optimum number of hidden layer nodes, as also the optimum value of the slope parameter of sigmoidal function. The role of adaptive sigmoidal activation function has been verified in constructive neural networks for better generalization performance and lesser training time.

Pravin Chandra

Papers

An activation function adapting training algorithm for sigmoidal feedforward networks

Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points

Measurement of Software Maintainability Using a Fuzzy Model

Feedforward sigmoidal networks - equicontinuity and fault-tolerance properties

Constructive neural networks: a review