國立高雄大學圖資館 |

Language: English

Back

Machine learning systems for multimo...

Kachele, Markus.

Machine learning systems for multimodal affect recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning systems for multimodal affect recognitionby Markus Kachele.
Author:	Kachele, Markus.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden :2020.
Description:	xix, 188 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-658-28674-3
ISBN:	9783658286743$q(electronic bk.)

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$q(electronic bk.)

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

188639
Machine learning.

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

Dewey Class. No.: 006.31

Machine learning systems for multimodal affect recognition
LDR:02622nmm a2200325 a 4500 001 573985
003 DE-He213
005 20191120041543.0
006 m d
007 cr nn 008maaau
008 201007s2020 gw s 0 eng d
020 $a 9783658286743$q(electronic bk.)
020 $a 9783658286736$q(paper)
024 7 $a 10.1007/978-3-658-28674-3 $2 doi
035 $a 978-3-658-28674-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .K2 2020
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .K11 2020
100 1 $a Kachele, Markus. $3 861491
245 1 0 $a Machine learning systems for multimodal affect recognition $h [electronic resource] / $c by Markus Kachele.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Vieweg, $c 2020.
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.
650 0 $a Machine learning. $3 188639
650 0 $a Pattern recognition systems. $3 183725
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a User Interfaces and Human Computer Interaction. $3 274517
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-658-28674-3
950 $a Computer Science (Springer-11645)