Semi-supervised SVM with fuzzy controlled cooperation of biology related algorithms : доклад, тезисы доклада | Научно-инновационный портал СФУ

Semi-supervised SVM with fuzzy controlled cooperation of biology related algorithms : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Informatics in Control, Automation and Robotics, ICINCO 2017; Madrid; Madrid

Год издания: 2017

Ключевые слова: bio-inspired algorithms, classification, constrained optimization, fuzzy controller, Semi-supervised learning, support vector machines

Аннотация: Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (SVM) are based on applying the margin maximization principle to both labelled and unlabelled examples. A new collective bionic algorithm, namely fuzzy controlled cooperation of biology related algorithms (COBRA-f), which solves constrained optimization problems, has been developed for semi-supervised SVM design. Firstly, the experimental results obtained by the two types of fuzzy controlled COBRA are presented and compared and their usefulness is demonstrated. Then the performance and behaviour of proposed semi-supervised SVMs are studied under common experimental settings and their workability is established.

Ссылки на полный текст

Издание

Журнал: ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics

Выпуск журнала: 1

Номера страниц: 64-71

Авторы

  • Akhmedova S. (Reshetnev Siberian State University of Science and Technology)
  • Semenkin E. (Reshetnev Siberian State University of Science and Technology)
  • Stanovov V. (Reshetnev Siberian State University of Science and Technology)

Вхождение в базы данных

Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.

Вы можете отметить интересные фрагменты текста, которые будут доступны по уникальной ссылке в адресной строке браузера.