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    Rights statement: ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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Cybernetics of the mind: learning individual's perceptions autonomously

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Cybernetics of the mind: learning individual's perceptions autonomously. / Angelov, Plamen Parvanov; Gu, Xiaowei; Iglesias, Jose et al.
In: IEEE Systems, Man, and Cybernetics Magazine, Vol. 3, No. 2, 04.2017, p. 6-17.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Angelov, PP, Gu, X, Iglesias, J, Ledezma, A, Sanchis, A, Sipele, O & Ramezani, R 2017, 'Cybernetics of the mind: learning individual's perceptions autonomously', IEEE Systems, Man, and Cybernetics Magazine, vol. 3, no. 2, pp. 6-17. https://doi.org/10.1109/MSMC.2017.2664478

APA

Angelov, P. P., Gu, X., Iglesias, J., Ledezma, A., Sanchis, A., Sipele, O., & Ramezani, R. (2017). Cybernetics of the mind: learning individual's perceptions autonomously. IEEE Systems, Man, and Cybernetics Magazine, 3(2), 6-17. https://doi.org/10.1109/MSMC.2017.2664478

Vancouver

Angelov PP, Gu X, Iglesias J, Ledezma A, Sanchis A, Sipele O et al. Cybernetics of the mind: learning individual's perceptions autonomously. IEEE Systems, Man, and Cybernetics Magazine. 2017 Apr;3(2):6-17. Epub 2017 Apr 17. doi: 10.1109/MSMC.2017.2664478

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei ; Iglesias, Jose et al. / Cybernetics of the mind : learning individual's perceptions autonomously. In: IEEE Systems, Man, and Cybernetics Magazine. 2017 ; Vol. 3, No. 2. pp. 6-17.

Bibtex

@article{6c6f5e16a71041ddb44469fd27d81ff1,
title = "Cybernetics of the mind: learning individual's perceptions autonomously",
abstract = "In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments.",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu and Jose Iglesias and Agapito Ledezma and Araceli Sanchis and Oscar Sipele and Ramin Ramezani",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2017",
month = apr,
doi = "10.1109/MSMC.2017.2664478",
language = "English",
volume = "3",
pages = "6--17",
journal = "IEEE Systems, Man, and Cybernetics Magazine",
publisher = "IEEE",
number = "2",

}

RIS

TY - JOUR

T1 - Cybernetics of the mind

T2 - learning individual's perceptions autonomously

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

AU - Iglesias, Jose

AU - Ledezma, Agapito

AU - Sanchis, Araceli

AU - Sipele, Oscar

AU - Ramezani, Ramin

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2017/4

Y1 - 2017/4

N2 - In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments.

AB - In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments.

U2 - 10.1109/MSMC.2017.2664478

DO - 10.1109/MSMC.2017.2664478

M3 - Journal article

VL - 3

SP - 6

EP - 17

JO - IEEE Systems, Man, and Cybernetics Magazine

JF - IEEE Systems, Man, and Cybernetics Magazine

IS - 2

ER -