Multiple Instance Learning for Emotion Recognition Using Physiological Signals

• Published in: IEEE transactions on affective computing Vol. 13; no. 1; pp. 389 - 407
• Main Authors: Romeo, Luca, Cavallo, Andrea, Pepa, Lucia, Bianchi-Berthouze, Nadia, Pontil, Massimiliano
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Algorithms; Ambiguity; Computational modeling; diverse density; Emotion recognition; Emotional factors; Emotions; Labeling; Machine learning; multiple instance learning; Pain; physiological signals; Physiology; support vector machine; Task analysis; time ambiguity

The problem of continuous emotion recognition has been the subject of several studies. The proposed affective computing approaches employ sequential machine learning algorithms for improving the classification stage, accounting for the time ambiguity of emotional responses. Modeling and predicting t...