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Machine learning systems for multimo...

Kachele, Markus.

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Machine learning systems for multimodal affect recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning systems for multimodal affect recognition/ by Markus Kachele.
Author:	Kachele, Markus.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2020.,
Description:	xix, 188 p. :ill., digital ;24 cm.
[NT 15003449]:	Classification and Regression Approaches -- Applications and Affective Corpora -- Modalities and Feature Extraction -- Machine Learning for the Estimation of Affective Dimensions -- Adaptation and Personalization of Classifiers -- Experimental Validation.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-658-28674-3
ISBN:	9783658286743

Machine learning systems for multimodal affect recognition
Kachele, Markus.

Machine learning systems for multimodal affect recognition[electronic resource] /by Markus Kachele. - Wiesbaden :Springer Fachmedien Wiesbaden :2020. - xix, 188 p. :ill., digital ;24 cm.

Classification and Regression Approaches -- Applications and Affective Corpora -- Modalities and Feature Extraction -- Machine Learning for the Estimation of Affective Dimensions -- Adaptation and Personalization of Classifiers -- Experimental Validation.

Markus Kachele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons. Contents Classification and Regression Approaches Applications and Affective Corpora Modalities and Feature Extraction Machine Learning for the Estimation of Affective Dimensions Adaptation and Personalization of Classifiers Experimental Validation Target Groups Lecturers and students of neuroinformatics, artificial intelligence, machine learning, human-machine interaction/affective computing Practitioners in the field of artificial intelligence and human-machine interaction The Author Dr. Markus Kachele is managing partner of Ikara Vision Systems, a spin-off of the German Research Center for Artificial Intelligence (DFKI) He focuses on bridging the gap between research and industrial applications in the fields of deep learning and computer vision.

ISBN: 9783658286743

Standard No.: 10.1007/978-3-658-28674-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .K2 2020

Dewey Class. No.: 006.31

Machine learning systems for multimodal affect recognition
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245 1 0 $a Machine learning systems for multimodal affect recognition $h [electronic resource] / $c by Markus Kachele.
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300 $a xix, 188 p. : $b ill., digital ; $c 24 cm.
505 0 $a Classification and Regression Approaches -- Applications and Affective Corpora -- Modalities and Feature Extraction -- Machine Learning for the Estimation of Affective Dimensions -- Adaptation and Personalization of Classifiers -- Experimental Validation.
520 $a Markus Kachele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons. Contents Classification and Regression Approaches Applications and Affective Corpora Modalities and Feature Extraction Machine Learning for the Estimation of Affective Dimensions Adaptation and Personalization of Classifiers Experimental Validation Target Groups Lecturers and students of neuroinformatics, artificial intelligence, machine learning, human-machine interaction/affective computing Practitioners in the field of artificial intelligence and human-machine interaction The Author Dr. Markus Kachele is managing partner of Ikara Vision Systems, a spin-off of the German Research Center for Artificial Intelligence (DFKI) He focuses on bridging the gap between research and industrial applications in the fields of deep learning and computer vision.
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