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IEEE International Conference on systems, Man and Cybernetics

作者： S. Al-Sharhan F. Karray W. Gueaieb Department of Systems Design Engineering University of Waterloo Ontario Canada Pattern Analysis and Machine Intelligence (PAMI) Lab Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

A new evolutionary algorithm for the constrained multiple destination routing problem is presented. The constrained multicast problem is characterized by a minimum cost multicast tree and a bounded end-to-end delay. It has been proven that this problem is NP-complete. The proposed algorithm is based on a soft computing technique that integrates in an efficient manner the merits of genetic algorithms and concepts of the competitive learning in the artificial neural networks literature. A population based learning algorithm is utilized, among other techniques, to construct a delay bounded multicast tree. A salient feature of the algorithm is the adaptive learning concept that achieves an efficient trade-off between the exploration and exploitation of the search space.

关键词： Computational intelligence Routing Communication networks Multicast algorithms Delay Evolutionary computation Costs Computer networks Genetic algorithms Artificial neural networks

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A hierarchical genetic algorithm for the design of beta basis function neural network

A hierarchical genetic algorithm for the design of beta basi...

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International Joint Conference on Neural Networks (IJCNN)

作者： C. Aouiti A.M. Alimi F. Karray A. Maalej Faculty of Sciences of Bizerta Universite de Carthage Tunisia Department of Electrical Engineering REGIM: Research Group on Intelligent Machines University of Sfax Sfax Tunisia Systems design Engineering Department Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada Department of Mechanical Engineering University of Sfax ENIS Sfax Tunisia

We propose an evolutionary neural network-training algorithm for beta basis function neural networks (BBFNN). Classic training algorithms for neural networks start with a predetermined network structure. Generally the network resulting from learning applied to a predetermined architecture is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BBFNN. In order to examine the performance of the proposed algorithm, they were used for the approximation problems. The results obtained are very satisfactory with respect to the relative error.

关键词： Genetic algorithms Algorithm design and analysis Neural networks Artificial neural networks Biological cells Neurons Chaos machine intelligence Laboratories Convergence

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Feature based decision fusion 2nd

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2nd International Conference on Advances in pattern Recognition, ICAPR 2001

作者： Wanas, Nayer M. Kamel, Mohamed S. Pattern Analysis and Machine Intelligence lab Department of Systems Design Engineering University of Waterloo WaterlooONN2L-3G1 Canada

ISBN: (纸本)3540417672

In this paper we present a new architecture for combining classifiers. This approach integrates learning into the voting scheme used to aggregate individual classifiers decisions. This overcomes the drawbacks of having static voting techniques. The focus of this work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. Test results show improvement in the overall classification rates over any individual classifier, as well as different static classifier-combining schemes. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Network architecture

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Decision fusion in neural network ensembles

Decision fusion in neural network ensembles

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International Joint Conference on Neural Networks (IJCNN)

作者： N.M. Wanas M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

ISBN: (纸本)0780370449

We present a comparison between different combining techniques in neural network ensembles. The main focus of this paper is on a new architecture that can be used in combining neural network ensembles. This architecture is based on training two neural networks to perform the aggregation. One network is trained to establish a confidence factor for each member of the ensemble for every training entry. The other network performs the aggregation of the ensemble to present the final decision. Both these networks evolve together during training. This approach is compared with standard fixed and trained combining schemes.

关键词： Intelligent networks Neural networks Testing Voting Clouds Image databases Glass Gaussian distribution Satellites Remote sensing

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An adaptive training algorithm for an ensemble of networks

An adaptive training algorithm for an ensemble of networks

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International Joint Conference on Neural Networks (IJCNN)

作者： N. Wanas L. Hodge M. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

ISBN: (纸本)0780370449

An ensemble of neural networks offers several advantages over classical single classifier systems when applied to complex pattern classification problems. However, the performance of the ensemble as a unit depends not only on the effective aggregation of the modules decisions, but also on the accuracy of the individual classification decisions of each module. The accuracy at the modular level is a result of the quality of training received by each module. This paper presents an adaptive training algorithm that can be used to direct the training of the individual modules so as to improve the classification accuracy and training efficiency of the ensemble.

关键词： Voting Neural networks Training data pattern analysis machine intelligence System analysis and design design engineering systems engineering and theory pattern classification Crosstalk

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Guest Editorial

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Intelligent Automation & Soft Computing 2000年第3期6卷 171-172页

作者： Mohamed Kamel[a] & Mohammad Jamshidi[b] [a] Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada [b] Nasa Center for Autonomous Control Engineering and Department of Electrical and Computer Engineering The University of New Mexico Albuquerque NM 87131 USA http://ace.tmm.edu

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Neural networks: the state of the art

Neural networks: the state of the art

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International Conference on Microelectronics, ICM

作者： M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

Artificial neural networks (ANNs) have demonstrated their success in many applications due to their ability to solve some problems with relative ease of use and the model-free property they enjoy. ANNs can solve problems without the need to understand or learn the analytical and statistical properties of the problem nor the solution steps. Research in ANNs has resulted in a variety of models and learning algorithms. In this paper, a brief review of recent advances in the field is presented. The paper then focuses on the recent work conducted by the author's group on modular neural networks. In particular, the paper discusses the different modular structures, modes of interactions, capabilities, co-operation among modules and fusion of their decisions. Performance of these models has proven to be superior to nonmodular neural networks.

关键词： Neural networks Artificial neural networks Computer errors Multilayer perceptrons Supervised learning Feedback pattern analysis machine intelligence System analysis and design design engineering

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Multiple classifier hierarchical architecture for handwritten Arabic character recognition

Multiple classifier hierarchical architecture for handwritte...

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International Joint Conference on Neural Networks (IJCNN)

作者： N.M. Wanas M.R. El-Sakka M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

Combining decisions from several classifiers can be used to improve on the results of handwritten characters recognition. There are different methods to combine these decisions, most of which are static. We present an architecture that integrates learning into the voting scheme used to aggregate individual decisions. The focus of the work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. The approach is tested on handwritten Arabic character recognition. The results showed an improvement over any individual classifier, as well as different static classifier combining schemes.

关键词： Character recognition Voting Handwriting recognition Neural networks Bayesian methods pattern recognition Aggregates Optical character recognition software Decision making Multi-layer neural network

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An information theoretic image-quality measure

An information theoretic image-quality measure

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： O. Elbadawy M.R. El-Sakka M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

Lossy image compression techniques aim at encoding images with a minimal representation. During this process, some visually useful information may be lost. Assessing the information loss in decompressed images is not an easy task. In this paper, a new quantitative image-quality measure is introduced. This new measure incorporates information theory into the most commonly used objective criterion (the mean square error). The new measure has been tested by experiments performed on a wide variety of images. The results show an increase in the correlation between subjective rating by human observers and the normalized mean square error after applying the new measure.

关键词： Image coding Testing Mean square error methods Humans Image quality Information theory Layout pattern analysis machine intelligence Laboratories

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EVOL: ensembles voting online

EVOL: ensembles voting online

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Auda M. Kamel Pattern Analysis And Machine Intelligence Lab Systems Design Engineering Department University of Waterloo Canada

Cooperation by voting is one of the popular modular neural network decision-making strategies. Ensemble classifiers are multiple identical modules which use voting for post-learning classification. This paper suggests a new cooperation scheme for ensembles which utilizes voting in the learning process itself. According to the suggested scheme, different modules would, automatically, focus on different regions in the input space. Hence, temporal crosstalk decreases and decision boundaries are drawn accurately in complex overlapping regions of the input space.

关键词： Voting Neural networks Decision making Multi-layer neural network Filtering Crosstalk pattern analysis machine intelligence System analysis and design design engineering

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