Добро пожаловать в клуб

Показать / Спрятать  Домой  Новости Статьи Файлы Форум Web ссылки F.A.Q. Логобург    Показать / Спрятать

       
Поиск   
Главное меню
ДомойНовостиСтатьиПостановка звуковФайлыДефектологияКнижный мирФорумСловарьРассылкаКаталог ссылокРейтинг пользователейЧаВо(FAQ)КонкурсWeb магазинШкольникамКарта сайта

Поздравляем!
Поздравляем нового Логобуржца ФАРМИК со вступлением в клуб!

Реклама

КНИЖНЫЙ МИР

Improved Predictive Clustering Tree Algorithm with Post Pruning   Purvi Prajapati and Amit Thakkar

Improved Predictive Clustering Tree Algorithm with Post Pruning

96 страниц. 2014 год.
LAP Lambert Academic Publishing
Multi label classification is a variation of single label classification problem where each instance is associated with more than one class label. The foremost unremarkably used approach to handle multi-label classification problem is to transfer multi-label problem into single label problems, where binary classifier is learned independently for every attainable class labels. However, multi-labeled data generally exhibit relationships between labels, but multi-label classification approach fails to take such relationships under consideration. It's understood that in this type of classification, labels co-relationship should be maintain. Label co-relationships can be visualized either in tree structure hierarchies or in DAG (Directed Acyclic Graph) structure hierarchies. These hierarchical arrangement of labels maintain the hierarchical constraint that is once an instance belongs to some class that automatically belongs to all its super classes. This book presents several variations to...
 
- Генерация страницы: 0.05 секунд -