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[Illinois]: Fish classification using back-propagation

By Lisa Sproat

University of Illinois at Urbana-Champaign

Trains a three-layered network of sigmoidal units using back-propagation to classify fish according to their lengths

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Version 1.0a - published on 19 Aug 2013

doi:10.4231/D32R3NX1X cite this

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Abstract

This tool trains a three-layered network of sigmoidal units using back-propagation to classify fish according to their lengths as shown below.

FIGURE 9.1 is a flow diagram illustrating a procedure for classification of fishes. In this case the fish species to be classified are salmon and trout. (Diagram after Duda et al. 2001; drawings courtesy of Bob Hines, U.S. Fish and Wildlife Service.)

FIGURE 9.2 models a three-layered, feedforward network used to classify fish by length There is 1 input unit x, 12 hidden units yi, and 3 output units z1, z2, and z3. The hidden, input, and output units are indexed by i (i= 1,.. .,12), j (j = 1), and k (k = 1,2,3), respectively. The input unit projects to the hidden units over the weights in matrix V (with elements vij). In this case, in which there is only one input unit, V is a column vector. The hidden units project to the output units over the weights in matrix U (with elements uki). The units are sigmoidal (see Chaper 6).

This tool is built from the MATLAB scripts for Tutorial on Neural Systems Modeling by Thomas J. Anastasio.

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References

Anastasio, T. J. (2009). Tutorial on Neural Systems Modeling. Sinauer Associates, Incorporated.

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Researchers should cite this work as follows:

  • Lisa Sproat (2013), "[Illinois]: Fish classification using back-propagation," http://nanohub.org/resources/fishbackprop. (DOI: 10.4231/D32R3NX1X).

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