@inproceedings{scholars1765,
           pages = {400--405},
         journal = {Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011},
           title = {Recognising complex patterns through a distributed multi-feature approach},
         address = {Malacca},
            year = {2011},
             doi = {10.1109/HIS.2011.6122139},
            note = {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},
        keywords = {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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856752376&doi=10.1109\%2fHIS.2011.6122139&partnerID=40&md5=46e0e8259741d67c4059f56addcec60e},
        abstract = {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. {\^A}{\copyright} 2011 IEEE.},
          author = {Amin, A. H. M. and Khan, A. I.},
            isbn = {9781457721502}
}