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Using support vector machines in fuzzy classification
Артикул Маркета
5951555050
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Т8 Издательские Технологии
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Печатная книга «Using support vector machines in fuzzy classification» в мягкой обложке, язык - английский. В книге 56 страниц, она напечатана на белой офсетной бумаге плотностью 80 г/м². Формат издания — 152x229 мм. Все книги аккуратно упаковываем в прочный трёхслойный непрозрачный картонный конверт.
Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification; in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress.
Fuzzy classification is one of methods used for pattern classification, which is germane to many engineering applications. An output from fuzzy classification is an assignment of patterns to fuzzy classes. There are several methods for fuzzy classification; in this paper we propose a new method based on the soft margin support vector machines classifier (C-SVM). These classifiers are based on statistical learning theory and they are widely used in pattern classification. In our approach, the decision boundary and slack variables obtained from C-SVM are used for the definition of a new optimization problem. The goal is to find an optimal parametrized transformation function T , which transforms the distance of a pattern from de-cision boundary to its membership degree. Quadratic programming is used to find suitable values for the parameters of T . The development of the full scope of this new fuzzy classification method is still in progress.
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Артикул Маркета
5951555050
Внешний вид товаров и/или упаковки может быть изменён изготовителем и отличаться от изображенных на Яндекс Маркете.Возрастное ограничение 18+
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