Evolutionary Computation Framework for Learning from Visual Examples

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{krawiec:2001:IPC,

• author = "Krzysztof Krawiec",
• title = "Evolutionary Computation Framework for Learning from Visual Examples",
• journal = "Image Processing and Communications",
• year = "2001",
• volume = "7",
• number = "3-4",
• pages = "85--96",
• keywords = "genetic algorithms, genetic programming, visual learning, genetic local search, learning from examples",
• ISSN = "1425-140X",
• URL = "http://www-idss.cs.put.poznan.pl/~krawiec/pubs/ipc2001.pdf",
• URL = "http://citeseer.ist.psu.edu/494563.html",
• size = "13 pages",
• abstract = "This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the comparison between the standard genetic programming (as defined by Koza [13]) and the genetic programming extended by local optimisation of solutions, so-called genetic local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.",
• notes = "http://wtie.atr.bydgoszcz.pl/ip&c/indexip&c.html",

}

Genetic Programming entries for Krzysztof Krawiec

Citations