An Evaluation of Algorithms for Single-Echo Biosonar Target Classification

A recent neuro-spiking coding scheme for feature extraction from biosonar echoes of various plants is examined with a variety of stochastic classifiers. Feature vectors derived are employed in 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, neuro-spike coding, non-parametric model, parametric model, Gaussian mixture, EM algorithm.

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: Turgay Temel, John Hallam
• Keywords: Artificial Intelligence, Gaussian Mixture, IJIT 2004, Stochastic Classifiers, Well-known Stochastic Classifiers