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检索条件"机构=Pattern Analysis and Machine Intelligence Group Department of Systems Design Engineering"

共 71 条记录，以下是21-30 订阅

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Multiple Cooperating Swarms for Data Clustering

Multiple Cooperating Swarms for Data Clustering

引用

IEEE Swarm intelligence Symposium, SIS

作者： Abbas Ahmadi Fakhri Karray Mohamed Kamel Systems Design Engineering Department Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada Electrical and Computer Engineering Department Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada

A new clustering technique by the use of multiple swarms is proposed. The proposed technique mimics the behavior of biological swarms which explore food situated in several places. We model the clustering problem using particle swarm optimization (PSO) approach. The proposed method considers multiple cooperating swarms to find centers of clusters. By assigning a portion of the solution space to each swarm, the exploration ability to find the solution is enhanced. Moreover, the cooperation among swarms increases the between-class distance. The proposed method outperforms k-means clustering as well as conventional PSO-based clustering techniques

关键词： Particle swarm optimization Data engineering design engineering Biology computing Birds Educational institutions Marine animals Space exploration Equations pattern analysis

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RBF-based real-time hierarchical intrusion detection systems

RBF-based real-time hierarchical intrusion detection systems

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ju Jiang Chunlin Zhang M. Kamel Pattern Analysis and Machine Intelligence Research Group Systems Design Department Engineering Faculty University of Waterloo Canada Systems Design Department University of Waterloo Canada

An intrusion detection system (IDS) is an art to detect network intrusions by monitoring the network traffic patterns. Generally, an IDS uses only a single-layer detection structure; therefore it cannot adjust its structure adaptively and automatically. In this paper, two hierarchical IDSs, the serial hierarchical and parallel hierarchical IDSs, are proposed. Both of them are based on radial basis function (RBF) neural networks. Because of the short training time and high accuracy of the RBF neural networks, two hierarchical IDSs can monitor network traffic in real-time, train new classifiers for novel intrusions automatically, and modify their structures adaptively after new classifiers are trained.

关键词： Real time systems Intrusion detection Neural networks Telecommunication traffic Computerized monitoring Computer networks Condition monitoring pattern analysis machine intelligence System analysis and design

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A connectionist model of human grasps and its application to robot grasping

A connectionist model of human grasps and its application to...

引用

International Conference on Neural Networks

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

Presents a connectionist model that can learn human grasping rules. The model domain is a taxonomy of manufacturing grasps. The input vector specifies task and object attributes such as dexterity and shape, and the model predicts the required grasp. Experiments show that the model is capable of learning grasping rules and predicting correct grasps for new objects that it has not been trained on. The authors finally discuss how this model can be used as part of a robotic system for grasping objects.

关键词： Humans Service robots Taxonomy Robot sensing systems Grippers Shape Predictive models Electrical equipment industry Robot control Neural networks

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Opposition-Based Q(λ) Algorithm

Opposition-Based Q(λ) Algorithm

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Shokri H.R. Tizhoosh M. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo ONT Canada Pattern Analysis and Machine Intelligence Laboratory Department of Electrical and Computer Engineering University of Waterloo ONT Canada

The problem of delayed reward in reinforcement learning is usually tackled by implementing the mechanism of eligibility traces. In this paper we introduce an extension of eligibility traces to solve one of the challenging problems in reinforcement learning. The concept of opposition traces is proposed in this work to deal with large state space problems in reinforcement learning applications. We combine the idea of opposition and eligibility traces to construct the opposition-based Q(lambda). The results are compared with the conventional Watkins' Q(lambda) and reflect a remarkable performance increase.

关键词： Learning Delay Acceleration Neural networks pattern analysis machine intelligence Laboratories System analysis and design design engineering systems engineering and theory

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Neural networks: the state of the art

Neural networks: the state of the art

引用

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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Decision fusion in neural network ensembles

Decision fusion in neural network ensembles

引用

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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Multiple classifier hierarchical architecture for handwritten Arabic character recognition

Multiple classifier hierarchical architecture for handwritte...

引用

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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A coordination mechanism for model-based multi-sensor planning

A coordination mechanism for model-based multi-sensor planni...

引用

IEEE International Symposium on Intelligent Control (ISIC)

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

This paper presents a multi-agent system for coordinating the deployment of multiple sensors in a modeled environment. The sensing task is the maximal sensor coverage of one or more targets in a scene and the position of each sensor is controlled by an autonomous agent. The agents rely on negotiation to achieve the level of coordination necessary to accomplish the given sensing task. Currently, the system focuses on the use of cameras for visual inspection tasks in which a single camera may be inadequate due to occluding objects in the scene, the number of targets to be observed or the sheer size of the target. The paper presents the negotiation mechanism developed for the multi-agent planning of the camera positions and illustrates its effectiveness by an example. Results show that the agents were able to autonomously position the cameras so as to allow for an acceptable level of coverage of the targets being observed.

关键词： Cameras Sensor systems Layout Inspection Intelligent sensors Sensor phenomena and characterization Control systems Art pattern analysis machine intelligence

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A Reinfrocement Learning Approach to Online Learning in Control

A Reinfrocement Learning Approach to Online Learning in Cont...

引用

International Conference on Control and Automation (ICCA)

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

In search for a reinforcement learning technique with the simplicity of tabular techniques and capability of dealing with continuous states like in fuzzy systems, we introduce a new approach, which we have called Adaptive Pseudo Fuzzy technique. The idea is to partition the state space with some window functions similar to fuzzy membership functions and learn the output values by changing the rules’ consequents directly. This is a kind of local function approximation similar to RBFs and CMACs, however, unlike RBFs it does not shift the basis, and unlike CMACs it does not go through complex coarse coding. This method represents the input as a fuzzy system and learns the output as a look-up-table. We employed this technique in Sarsa(λ) to build a general online controller and examined it for a pole-balancing problem.

关键词： Fuzzy systems Learning State-space methods Function approximation

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An adaptive training algorithm for an ensemble of networks

An adaptive training algorithm for an ensemble of networks

引用

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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