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      Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)

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      Engineering Geology
      Elsevier BV

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          Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy

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            Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

            J V Tu (1996)
            Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
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              Landslide hazard assessment: summary review and new perspectives

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                Author and article information

                Journal
                Engineering Geology
                Engineering Geology
                Elsevier BV
                00137952
                July 2005
                July 2005
                : 79
                : 3-4
                : 251-266
                Article
                10.1016/j.enggeo.2005.02.002
                48c7787d-48ef-4325-805e-81560012ffba
                © 2005

                http://www.elsevier.com/tdm/userlicense/1.0/

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