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A QA-TSK fuzzy model vs evolutionary decision trees towards nonlinear action pattern recognition

Theodoridis, T and Agapitos, A and Hu, H (2010) A QA-TSK fuzzy model vs evolutionary decision trees towards nonlinear action pattern recognition. In: UNSPECIFIED, ? - ?.

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Abstract

A comparison among three linear methodologies, a novel auto-adjusted fuzzy quadruple TSK model (QA-TSK) and two evolutionary decision tree representations, is presented in this paper. The three architectures make use of a vast number of primitives1 utilised to reconfigure and evolve their internal structures of the classifier models so that to discriminate among spatial physical activities. Such primitives like statistical features employ a twofold role, initially to model the data set in a dimensionality reduction preprocessing and finally to exploit these attributes to recognise pattern actions. The performance statistics are being utilised for remote surveillance within a smart environment incorporating an ubiquitous 3D marker based tracker which acquires the timeseries data streams, whereas activity recognition statistics are being generated through an off-line process. ©2010 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2010 IEEE International Conference on Information and Automation, ICIA 2010
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 17 Dec 2014 11:33
Last Modified: 23 Jan 2019 00:17
URI: http://repository.essex.ac.uk/id/eprint/9086

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