%T Recognising complex patterns through a distributed multi-feature approach %O cited By 0; Conference of 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011 ; Conference Date: 5 December 2011 Through 8 December 2011; Conference Code:88378 %D 2011 %C Malacca %R 10.1109/HIS.2011.6122139 %J Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011 %L scholars1765 %P 400-405 %A A.H.M. Amin %A A.I. Khan %X Multiple-feature implementation enables a holistic approach towards pattern recognition procedure that takes into consideration all significant features, which represent a particular set of complex patterns, such as images and sensor readings. This intends to reduce the bias effect of selecting only a single feature for classification/recognition purposes. In this paper we demonstrate the effectiveness of this approach in comparison with some well-known multi-feature pattern recognition techniques. Our approach benefits from having a set of distributed computational networks working together, forming a distributed recognition network that alleviates the issue of scalability against increasing number of features to be considered. In addition, the use of our proposed single-cycle learning distributed pattern recognition algorithm shows a significant reduction in training samples to achieve comparable accuracy. © 2011 IEEE. %K Bias effects; Complex pattern; Computational networks; Distributed patterns; Holistic approach; Pattern recognition procedures; Pattern recognition techniques; Sensor readings; Single cycle; Training sample, Feature extraction; Intelligent systems, Distributed computer systems