TY  - CONF
SN  - 9781457721502
N2  - 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.
KW  - Bias effects; Complex pattern; Computational networks; Distributed patterns; Holistic approach; Pattern recognition procedures; Pattern recognition techniques; Sensor readings; Single cycle; Training sample
KW  -  Feature extraction; Intelligent systems
KW  -  Distributed computer systems
TI  - Recognising complex patterns through a distributed multi-feature approach
Y1  - 2011///
ID  - scholars1765
A1  - Amin, A.H.M.
A1  - Khan, A.I.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856752376&doi=10.1109%2fHIS.2011.6122139&partnerID=40&md5=46e0e8259741d67c4059f56addcec60e
N1  - 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
CY  - Malacca
EP  - 405
SP  - 400
AV  - none
ER  -