Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Мультиномиальная логистическая регрессия× | Методы агрегирования рангов× | |
|---|---|---|
| Область≠ | Эконометрика | Принятие решений |
| Семейство≠ | Regression model | Machine learning |
| Год появления≠ | 1974 | 2001 |
| Автор метода≠ | McFadden | Dwork, Kumar, Naor & Sivakumar |
| Тип≠ | Multinomial logistic regression | Combinatorial ranking method |
| Основополагающий источник≠ | McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503 | Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613–622. DOI ↗ |
| Другие названия | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon | Rank Fusion, Order Aggregation, Preference Aggregation, Sıralama Birleştirme |
| Связанные≠ | 5 | 2 |
| Сводка≠ | Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category. | Rank Aggregation is a family of methods that combine multiple ranked lists of alternatives into a single consensus ranking. Formally studied in the context of web search by Dwork, Kumar, Naor, and Sivakumar (2001), these methods address the problem of synthesizing divergent preference orderings from multiple sources — such as search engines, expert judges, or voter ballots — into one coherent, representative ordering that minimizes overall disagreement across the input rankings. |
| ScholarGateНабор данных ↗ |
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