Evolutionary algorithms for the design of neural network classifiers for the classification of pain intensity : научное издание

Mamontov D.; Polonskaia I.; Skorokhod A.; Semenkin E.; Kessler V.; Schwenker F.

Дополнительные ссылки: eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: Evolutionary algorithms for the design of neural network classifiers for the classification of pain intensity : научное издание / D. Mamontov [et al.]. - Текст : непосредственный // Lecture Notes in Computer Science (см. в книгах). - 2019. - Т.11377 LNAI, № . - P. 84-100. - ISSN 0302-9743. - ISSN 1611-3349.

Аннотация: In this paper we present a study on multi-modal pain intensity recognition based on video and bio-physiological sensor data. The newly recorded SenseEmotion dataset consisting of 40 individuals, each subjected to three gradually increasing levels of painful heat stimuli, has been used for the evaluation of the proposed algorithms. We propose and evaluated evolutionary algorithms for the design and adaptation of the structure of deep artificial neural network architectures. Feedforward Neural Network and Recurrent Neural Network have been considered for the optimisation by using a Self-Configuring Genetic Algorithm (SelfCGA) and Self-Configuring Genetic Programming (SelfCGP).

Год издания: 2019

Авторы: Mamontov D. , Polonskaia I. , Skorokhod A. , Semenkin E. , Kessler V. , Schwenker F.

Издательство: Springer-Verlag GmbH

Источник: Lecture Notes in Computer Science (см. в книгах)

Выпуск: Т.11377 LNAI

Номера страниц: 84-100

Держатель оригинала документа: Institute of Neural Information Processing,Ulm University, Reshetnev Siberian State University of Science and Technology

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Evolutionary algorithms for the design of neural network classifiers for the classification of pain intensity : научное издание
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