Mattam Niranjan

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Selection of features that will permit accurate pattern clas-siication is, in general, a diicult task. However, if a particular data set is represented by discrete valued features, it becomes possible to determine empirically the contribution that each feature makes to the discrimination between classes. We describe how to calculate the maximum(More)
We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets(More)
This report investigates the use of expected attainable discrimination (EAD) as a measure to select discrete valued features in two-class prediction problems. In essence, EAD tells us the performance we could expect to achieve with a simple histogram probability density model of a given dataset. For discrete valued features, this kind of density model is(More)
F. Fallside We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections. This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L 2-norm(More)
We describe an upper bound on the accuracy (in the ROC sense) attainable in two-alternative forced choice risk prediction, for a speciic set of data represented by discrete features. By accuracy, we mean the probability that a risk prediction system will correctly rank a randomly chosen high risk case and a randomly chosen low risk case. We also present(More)
A novel method is described for obtaining superior classiication performance over a variable range of classiication costs. By analysis of a set of existing classiiers using a receiver operating characteristic (ROC) curve, a set of new realisable classiiers may be obtained by a principled random combination of two of the existing classiiers. These classiiers(More)
Summary form only given, as follows. Gradient descent has been used with much success to train connectionist models in the form of the error propagation networks of Rumelhart, Hinton, and Williams. In these nets the output of a node is a nonlinear function of the weighted sum of the activations of other nodes. This type of node defines a hyperplane in the(More)
A study was conducted to evaluate the effect of feeding different metabolizable energy (ME) and crude protein (CP) levels on performance of Aseel chicken during 0 to 8 weeks of age (Juvenile phase). At 1 day old, 432 chicks were randomly distributed into nine groups. Each group had 48 chicks distributed into eight replicates with six birds in each.(More)
This paper explores the potential for the application of neurocomputing technology to the domain of post-operative liver transplant monitoring. The investigation compares a neural network model with two classical statistical techniques using biochemical information obtained from a set of liver transplant patients. Each approach combines the results of a(More)