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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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, ANN",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.ps.Z",
• size = "11 pages",
• abstract = "We apply Genetic Programming (GP) to the development of a processing tree for the classification of features extracted from images: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. No constraints are placed upon size, shape, or order of processing within the network. This network is used to classify feature vectors extracted from IR imagery into target/nontarget categories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. This represents a significant scaling up from the problems to which GP has been applied to date. Two experiments are performed: in the first set, we input classical statistical image features and minimize misclassification of target and non-target samples. In the second set of experiments, GP is allowed to form it's own feature set from primitive intensity measurements. 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 achieves higher performance with reduced computational requirements. The contributions of GP schemata, or subtrees, to the performance of generated trees are examined.",
• 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.",

}

Genetic Programming entries for Walter Alden Tackett

Citations