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An Evaluation of Algorithms for Single-Echo Biosonar Target Classification

Authors: Turgay Temel, John Hallam


A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.

Keywords: classification, parametric model, EM algorithm, neuro-spike coding, non-parametricmodel, Gaussian mixture

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