Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study

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@Article{bhattacharyya:1998:DS,

• author = "Siddhartha Bhattacharyya and Parag C. Pendharkar",
• title = "Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study",
• journal = "Decision Sciences",
• year = "1998",
• volume = "29",
• number = "4",
• pages = "871--899",
• month = "Fall",
• keywords = "genetic algorithms, genetic programming, Discriminant Analysis, Inductive Learning, Machine Learning, and Neural Networks",
• ISSN = "00117315",
• URL = "http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf",
• size = "45 pages",
• abstract = "This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data is used to examine how the different learning techniques perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data has been previously used in the comparative evaluation of a number of techniques and helps relate our findings across a range of discrimination techniques.",
• notes = "http://www.decisionsciences.org/dsj/ (USPS 884860) http://www.decisionsciences.org/dsj/Vol29_4/29_4_871.htm",

}

Genetic Programming entries for Siddhartha Bhattacharyya Parag C Pendharkar

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