relation: https://khub.utp.edu.my/scholars/1765/
title: Recognising complex patterns through a distributed multi-feature approach
creator: Amin, A.H.M.
creator: Khan, A.I.
description: 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.
date: 2011
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Amin, A.H.M. and Khan, A.I.  (2011) Recognising complex patterns through a distributed multi-feature approach.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856752376&doi=10.1109%2fHIS.2011.6122139&partnerID=40&md5=46e0e8259741d67c4059f56addcec60e
relation: 10.1109/HIS.2011.6122139
identifier: 10.1109/HIS.2011.6122139