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pro vyhledávání: '"Markus Kächele"'
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
The methodological advancements are validated based on benchmark datasets that were collected for the task at hand either from open repositories such as the UCI machine learning repository or they were artificially created.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::731514e385e1c3ed217cf17663f0f3ec
https://doi.org/10.1007/978-3-658-28674-3_8
https://doi.org/10.1007/978-3-658-28674-3_8
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
In this chapter, a brief overview of affective categories is given before the utilized data collections are introduced. Finally, a few points regarding corpus design and the annotation process are discussed.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0fe2cd955e91f332161f27930b89163
https://doi.org/10.1007/978-3-658-28674-3_3
https://doi.org/10.1007/978-3-658-28674-3_3
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
The recognition of affective states can be further divided into recognition of fixed categories such as happy or sad (compare Section 3.1) and continuous estimations according to affective dimensions such as arousal or valence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::748c4fd60d357bc9cc304b2cf9cf4550
https://doi.org/10.1007/978-3-658-28674-3_5
https://doi.org/10.1007/978-3-658-28674-3_5
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fb6e78cc803fe756572757c74727367
https://doi.org/10.1007/978-3-658-28674-3_10
https://doi.org/10.1007/978-3-658-28674-3_10
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
In this chapter methods for the personalization and adaptation of classification and regression models are presented. The idea of those approaches is to improve the quality of classification/regression models in cases in which no additional labeled t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c06bb74a659f0c4f544c8085a1330308
https://doi.org/10.1007/978-3-658-28674-3_6
https://doi.org/10.1007/978-3-658-28674-3_6
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b18ccf396c4ab5daae2e3b5fdd3b1a2e
https://doi.org/10.1007/978-3-658-28674-3_9
https://doi.org/10.1007/978-3-658-28674-3_9
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7be6e2438db242ce92dc769d2c3ec25d
https://doi.org/10.1007/978-3-658-28674-3_1
https://doi.org/10.1007/978-3-658-28674-3_1
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
In this chapter, the experimental validation of the pain intensity estimation systems is presented. First, discrete pain recognition is discussed. The setting allows experiments of the “binary kind”, i.e. pain vs. no pain, but also those with a m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d9d9d3b8e9f3bebbe8e61f7cb838510
https://doi.org/10.1007/978-3-658-28674-3_7
https://doi.org/10.1007/978-3-658-28674-3_7
Autor:
Markus Kächele
Publikováno v:
Machine Learning Systems for Multimodal Affect Recognition ISBN: 9783658286736
In this chapter, the modalities that are relevant for affect recognition in the scope of this work are presented, together with pre-processing steps and feature choices. The discussed modalities are audio, video and a number of biophysiological chann
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be3878bb468ecb95a955b5392c3fcd5e
https://doi.org/10.1007/978-3-658-28674-3_4
https://doi.org/10.1007/978-3-658-28674-3_4