Predicting students' happiness from physiology, phone, mobility, and behavioral data

2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015

Volumn , Issue , 2015, Pages 222-228

  Jaques, Natasha ^a   Taylor, Sara ^a   Azaria, Asaph ^a   Ghandeharioun, Asma ^a   Sano, Akane ^a   Picard, Rosalind ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

happiness; machine learning; wellbeing

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTELLIGENT COMPUTING; LEARNING ALGORITHMS; PHYSIOLOGICAL MODELS; PHYSIOLOGY; STUDENTS;

CLASSIFICATION ACCURACY; ENSEMBLE CLASSIFICATION; GAUSSIAN MIXTURE MODEL; HAPPINESS; MACHINE LEARNING METHODS; PHYSIOLOGICAL SIGNALS; SELECTION TECHNIQUES; WELLBEING;

LEARNING SYSTEMS;

EID: 84964033734     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACII.2015.7344575     Document Type: Conference Paper

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