Форма представления | Статьи в зарубежных журналах и сборниках |
Год публикации | 2022 |
Язык | английский |
|
Габидуллина Зульфия Равилевна, автор
|
Библиографическое описание на языке оригинала |
Gabidullina, Z.R. Design of the Best Linear Classifier for Box-Constrained Data Sets. Lecture Notes in Computational Science and Engineering
Том: 141
Страницы: 109 - 124 |
Аннотация |
We construct a binary linear classifier for n-dimensional data sets with the special box-constrained structure. Data sets with this structure arise naturally in many real-world areas. We apply a linear separability criterion proposed in J. Optim. Theory Appl. (2012, https://doi.org/10.1007/s10957-012-0155-x). The Minkowski difference of the two data sets allows us to reduce a two-class classification problem to the problem in more easy to solve form. The greatest benefit of this reduction is that it allows to compute the parameters of a linear binary classifier by way of exact formulas. For this reason, a proposed framework has low computational costs. We rigorously prove that the developed linear classification model provides the possibility of constructing the data separator (or pseudo-separator) which really has the best estimate of its thickness. There are studied both regular and singular cases of separability arising in the theory and practice of linear classification of data sets. |
Ключевые слова |
data classification, separator, pseudo-separator, thickness of the geometric margin |
Название журнала |
Lecture Notes in Computational Science and Engineering
|
URL |
https://link.springer.com/chapter/10.1007/978-3-030-87809-2_9 |
Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=271607 |
Полная запись метаданных |
Поле DC |
Значение |
Язык |
dc.contributor.author |
Габидуллина Зульфия Равилевна |
ru_RU |
dc.date.accessioned |
2022-01-01T00:00:00Z |
ru_RU |
dc.date.available |
2022-01-01T00:00:00Z |
ru_RU |
dc.date.issued |
2022 |
ru_RU |
dc.identifier.citation |
Gabidullina, Z.R. Design of the Best Linear Classifier for Box-Constrained Data Sets. Lecture Notes in Computational Science and Engineering
Том: 141
Страницы: 109 - 124 |
ru_RU |
dc.identifier.uri |
https://repository.kpfu.ru/?p_id=271607 |
ru_RU |
dc.description.abstract |
Lecture Notes in Computational Science and Engineering |
ru_RU |
dc.description.abstract |
We construct a binary linear classifier for n-dimensional data sets with the special box-constrained structure. Data sets with this structure arise naturally in many real-world areas. We apply a linear separability criterion proposed in J. Optim. Theory Appl. (2012, https://doi.org/10.1007/s10957-012-0155-x). The Minkowski difference of the two data sets allows us to reduce a two-class classification problem to the problem in more easy to solve form. The greatest benefit of this reduction is that it allows to compute the parameters of a linear binary classifier by way of exact formulas. For this reason, a proposed framework has low computational costs. We rigorously prove that the developed linear classification model provides the possibility of constructing the data separator (or pseudo-separator) which really has the best estimate of its thickness. There are studied both regular and singular cases of separability arising in the theory and practice of linear classification of data sets. |
ru_RU |
dc.language.iso |
ru |
ru_RU |
dc.subject |
data classification |
ru_RU |
dc.subject |
separator |
ru_RU |
dc.subject |
pseudo-separator |
ru_RU |
dc.subject |
thickness of the geometric margin |
ru_RU |
dc.title |
Design of the Best Linear Classifier for Box-Constrained Data Sets. |
ru_RU |
dc.type |
Статьи в зарубежных журналах и сборниках |
ru_RU |
|