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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Tackett93,

• author = "Walter Alden Tackett",
• title = "Genetic Generation of {``}Dendritic{''} Trees for Image Classification",
• booktitle = "World Congress on Neural Networks, WCNN'93",
• publisher = "Lawrence Erlbaum Ass., Inc.",
• publisher_address = "Hillsdale, NJ, USA",
• pages = "IV 646--649",
• year = "1993",
• month = "11-15 " # jul,
• address = "Portland, Oregon, USA",
• 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.",
• 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

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