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Today ensemble learning techniques became more interested in the field of predictive modelling. It is an effective technique which combines various learning algorithms so as to improve the overall prediction accuracy. The Ensemble technique works on a philosophy that a group of experts gives more accurate decisions as compared to a single expert. Ensemble modelling combines the set of classifiers...
A criterium for classification of hybrid systems is presented that is based on their input-output behaviour. Checking the criterium is possible with measurements but without a model of the system. The number of possible differentiations of the output signal as a response to any smooth input signal gives a continuity index for the system. All systems with a continuity index less than infinity can be...
This paper proposes a Session Initiation Protocol (SIP) message classification and prioritization scheme. This three-priority classification of SIP messages is implemented in Internet protocol Multimedia Subsystem (IMS). Its effectiveness is verified through the analytical and simulation modelling of IMS using a queuing theory. The proposed IMS model is analysed in the normal load and overload conditions...
This paper presents a new feature selection method which aims to effectively and efficiently map remote sensing data. An automated texture-based modelling procedure of forest structure variables is at the core of our approach. We show that texture features that are highly correlated to genuine physical parameters of forest structure have potential for building reliable classifiers. We demonstrate...
This paper presents a new method for rapid simulation model generation using cladistics, a technique commonly used for evolutionary analysis in biology. In this research, cladistics is adopted as a basis for classification of problems typically found in manufacturing systems particularly assembly lines. The basic premise in this research is that manufacturing systems evolve (e.g. from craft production...
The design and the making of mouldings in historic architecture is a good example of how complex relations of geometric features to semantic ones can be. Can architects who deal with historic artefacts, and who practice with computer solutions from the engineering world, still handle knowledge before handling geometry? This paper presents two very different case studies through which comprehensive...
For a classification problem, noise in real-world data can dramatically lower the predictive accuracy of a learner and increase the time in building model. Researchers have proved that preprocessing noise before learning can bring more advantages. Previous work mostly focus on class noise detection for the difficulties of attribute noise detection. In this paper, we present a cluster based noise detection...
The following topics are dealt with: machine vision; image processing; classification; face analysis; modelling; motion analysis; sequence analysis; feature extraction; medical imaging; biomedical imaging.
Weather radars are essential sensors for observation of precipitation in the troposphere and play a major part in weather forecasting and hydrological modelling. Clutter caused by non-standard wave propagation is a common problem in weather radar applications, and in this paper a method for prediction of this type of weather radar clutter is presented. The method uses a wave propagator to identify...
The following topics are dealt with: image processing; signal processing; biomedical engineering; fuzzy theory; soft computing; fuzzy application; feature extract; classification; and modelling.
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