Genetic Generation of ``Dendritic'' Trees for Image Classification

by

Tackett, W., A.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: Proceedings of WCNN93 (Conference proceedings), 1993, p. IV 646-649
Keywords:genetic algorithms, genetic programming, connectionism, cogann
Abstract:
ABSTRACT Genetic Programming (GP) [GP] is an adaptive method for generating executable programs from labeled training data. It differs from the conventional methods of Genetic Algorithms [GA] 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, [CS] the binary tree classifier and the Backpropagation neural network. [NN] The GP network acheives higher performance with reduced computational requirements. see also ftp://ftp.mad-scientist.com/pub/genetic-programming/papers/.message GP.feature.discovery.ps.Z
Internet search:Search Google
Search Google Scholar
Search Citeseer using Google
Search Google for PDF
Search Google Scholar for PDF
Search Citeseer for PDF using Google

Review item:

Mark as doublet (will be reviewed)

Print entry




BibTex:
@InProceedings{Tackett93,
  author =       "Walter Alden Tackett",
  title =        "Genetic Generation of {``}Dendritic{''} Trees for
                 Image Classification",
  booktitle =    "Proceedings of WCNN93",
  publisher =    "IEEE Press",
  pages =        "IV 646--649",
  year =         "1993",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming,
                 connectionism, cogann",
  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.

                 see also
                 ftp://ftp.mad-scientist.com/pub/genetic-programming/papers/.message
                 GP.feature.discovery.ps.Z",
}