Genetic Generation of ``Dendritic'' Trees for Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Tackett93,
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author = "Walter Alden Tackett",
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title = "Genetic Generation of {``}Dendritic{''} Trees for
Image Classification",
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booktitle = "World Congress on Neural Networks, WCNN'93",
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publisher = "Lawrence Erlbaum Ass., Inc.",
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publisher_address = "Hillsdale, NJ, USA",
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pages = "IV 646--649",
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year = "1993",
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month = "11-15 " # jul,
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address = "Portland, Oregon, USA",
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keywords = "genetic algorithms, genetic programming,
connectionism, cogann",
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abstract = "ABSTRACT Genetic Programming (GP) is an adaptive
method for generating executable programs from labeled
training data. It differs from the conventional methods
of Genetic Algorithms because it manipulates tree
structures of arbitrary size and shape rather than
fixed length binary strings. We apply GP to the
development of a processing tree with a dendritic, or
neuron-like structure: measurements from a set of input
nodes are weighted and combined through linear and
nonlinear operations to form an output response. Unlike
conventional neural methods, no constraints are placed
upon size, shape, or order of processing withing the
network. This network is used to classify feature
vectors extracted from IR imagery into target/nontarget
catagories using a database of 2000 training samples.
Performance is tested against a separate database of
7000 samples. For purposes of comparison, the same
training and test sets are used to train two other
adaptive classifier systems, the binary tree classifier
and the Backpropagation neural network. The GP network
acheives higher performance with reduced computational
requirements.",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.ps.Z",
- }
Genetic Programming entries for
Walter Alden Tackett
Citations