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What is Gaussian Mixture Model (GMM)

Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics
A probabilistic model used to represent data as a mixture of normal distributions. It is commonly used for unsupervised learning and clustering, which means that clusters can be created without labels. It is similar to k-means clustering, except that when used for recognition, it outputs the probability that a new data point belongs to a cluster, not a binary value.
Published in Chapter:
Developing Robot Emotions through Interaction with Caregivers
Angelica Lim (Kyoto University, Japan) and Hiroshi G. Okuno (Kyoto University, Japan & Waseda University, Japan)
DOI: 10.4018/978-1-4666-7278-9.ch015
Abstract
In this chapter, the authors explore social constructivist theories of emotion, which suggest that emotional behaviors are developed through experience, rather than innate. The authors' approach to artificial emotions follows this paradigm, stemming from a relatively young field called developmental or ‘epigenetic' robotics. The chapter describes the design and implementation of a robot called MEI (multimodal emotional intelligence) with an emotion development system. MEI synchronizes to humans through voice and movement dynamics, based on mirror mechanism-like entrainment. Via typical caregiver interactions, MEI associates these dynamics with its physical feeling, e.g. distress (low battery or excessive motor heat) or flourishing (homeostasis). Our experimental results show that emotion clusters developed through robot-directed motherese (“baby talk”) are similar to adult happiness and sadness, giving evidence to constructivist theories.
Full Text Chapter Download: US $37.50 Add to Cart
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Healthcare-Internet of Things and Its Components: Technologies, Benefits, Algorithms, Security, and Challenges
It is a probabilistic model in which, it is assumed that all the data points are generated from a miture of Gaussian distributions with unknown parameter.
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