Essec\Faculty\Model\Contribution {#2188
  #_index: "academ_contributions"
  #_id: "3466"
  #_source: array:26 [
    "id" => "3466"
    "slug" => "comparison-of-the-predictability-of-a-neural-network-with-retropropagation-with-those-using-linear-regression-logistic-and-a-i-d-methods-for-direct-marketing-scoring"
    "yearMonth" => "1998-01"
    "year" => "1998"
    "title" => "Comparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring"
    "description" => "DESMET, P. (1998). Comparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring. Dans: <i>Bio-Mimetic Approaches in Management Science</i>. 1st ed. Kluwer Academic Publishers, pp. 61-75."
    "authors" => array:1 [
      0 => array:3 [
        "name" => "DESMET Pierre"
        "bid" => "B00000155"
        "slug" => "desmet-pierre"
      ]
    ]
    "ouvrage" => "Bio-Mimetic Approaches in Management Science"
    "keywords" => []
    "updatedAt" => "2021-09-06 14:06:32"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => "61-75"
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Chapitres"
      "en" => "Book chapters"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "Par rapport aux méthodes statistiques classiques (régression linéaire, régression logistique, segmentation, analyse discriminante), les avantages des réseaux de neuronaux à rétropropagation sont connus et nombreux (effets non linéaires, pas d'hypothèses sur la distribution des variables, résistance aux valeurs manquantes ou aberrantes). Cependant les conditions de mise en oeuvre et la supériorité réelle en prédiction ne sont encore que peu documentées. L'article relève les recherches précédentes sur les analyses comparatives effectuées en marketing et présente les résultats obtenus pour la prédiction d'un comportement dans le domaine de la collecte de fonds."
      "en" => "In comparison with other usual statistical methods (MCO, logistic regression, discriminant analysis, AID), advantages of neural network with retropropagation are numerous and well known (non linear effects, distribution free variables, low sensibility to outliers or missing variables). However, implementation and efficiency have not yet received a strong interest. The paper reviews comparative analyses and presents results obtained for prediction of a behaviour in fund-raising."
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2024-07-16T07:22:04.000Z"
    "docTitle" => "Comparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring"
    "docSurtitle" => "Book chapters"
    "authorNames" => "<a href="/cv/desmet-pierre">DESMET Pierre</a>"
    "docDescription" => "<span class="document-property-authors">DESMET Pierre</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">1998</span>"
    "keywordList" => ""
    "docPreview" => "<b>Comparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring</b><br><span>1998-01 | Book chapters </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Comparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 9.200829
  +"parent": null
}