Knowledge representation with SOUL

One of the most important fields of affective computing is related to the hard problem of emotion recognition. At present, there are several approaches to the problem of automatic emotion recognition based on different methods, like Bayesian classifiers, Support Vector Machines, Linear Discriminant Analysis, Neural Networks or k-Nearest Neighbors, which classify emotions using several features obtained from facial expressions, body gestures, speech or different physiological signals. In this paper, we propose a Semantic Classifier as a new, simple and efficient approach to the problem of automatic emotion recognition. The implementation of the Semantic Classifier is based on the basic, natural principles used to decrease the complexity of problems found in n-dimensional spaces: discretization, structure identification and semantic optimization. The proposed classifier exhibits some self-organizing features and supports learning by repetition, generalization and specialization. It will be used to implement a distributed and robust system for emotion recognition.

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“…The problem was to interpret a complex, natural language flight query and to provide the required information about available flights [16].…”

Section: Introductionmentioning

confidence: 99%

Semantic Classifier for Affective Computing

Stanojevic

Vraneš

2008

2008 International Conference on Computational Intelligence for Modelling Control &Amp; Automation

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“…The techniques of knowledge representation were discussed and advanced in the past 20 years [16][17][18][19][20][21], such as Ontology, Petri Net, Semantic Network, Frame, Concept Mapping and Knowledge Map. Ontology is a formal and explicit specification of a shared conceptualization of a domain of interest.…”

Section: Introductionmentioning

confidence: 99%

Study on spatial knowledge representation and reasoning based on Bayesian networks

Huang

et al. 2009

SPIE Proceedings

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Spatial information plays an essential role on the progress of science and technology, and has a profound impact on economic growth and society progress in the twenty-first century. Spatial knowledge representation and reasoning are very important for us to utilize spatial information. In this paper, a review is presented on spatial knowledge representation and reasoning. And then we propose a method of spatial knowledge representation and reasoning based on Bayesian networks. We focused on how to represent spatial relationship, spatial objects and spatial features by using Bayesian networks. Let spatial features (or spatial objects, spatial relationships) as variables or the nodes in Bayesian network, let directed edges present the relationships between spatial features, and the relevancy intensity can be regarded as confidence between the variables (the same as probability parameter in Bayesian network). Accordingly, the problem of spatial knowledge representation will be changed to the problem of learning Bayesian networks. The experimental results are given to verify the practical feasibility of the proposed methodology. Eventually, we conclude with a summary and a statement of future work.

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Applying Neural Networks to Knowledge Representation and Determination of Its Meaning

Stanojevic

Vraneš

2007

Lecture Notes in Computer Science

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Knowledge representation with SOUL

Cited by 16 publications

References 27 publications

Semantic Classifier for Affective Computing

Semantic Classifier for Affective Computing

Study on spatial knowledge representation and reasoning based on Bayesian networks

Applying Neural Networks to Knowledge Representation and Determination of Its Meaning

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