System identification: theory for the user
System identification: theory for the user
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identification of staphylococcus aureus infections by volatile chemical headspace analysis
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
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This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.