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检索条件"机构=Pattern Analysis and Machine Intelligence Laboratory Systems Design Engineering"

共 79 条记录，以下是31-40 订阅

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Weighted combination of neural network ensembles

Weighted combination of 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

There exist numerous schemes and methods to determine the output of an ensemble of classifiers. The most common approach being the majority vote. Furthermore, we might expect that an improvement can be achieved if there is a method by which we may weigh the members of the ensemble according to their individual performance. The feature based approach presented an architecture that tries to approach this target. However, if there is a way that the final classification may influence these weights we should expect an increased performance in the overall classification task. In this paper we present a new training algorithm that utilizes a feedback mechanism to iteratively improve the classification capability of the feature based approach. This approach is compared with the standard training method as well as standard aggregation schemes for combining classifier ensembles. Empirical results show that this architecture improved on classification accuracy.

关键词： Neural networks Voting Detectors pattern analysis machine intelligence System analysis and design design engineering systems engineering and theory Iterative algorithms Jacobian matrices

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Fuzzy reinforcement learning for embedded soccer agents in a multi-agent context

Fuzzy reinforcement learning for embedded soccer agents in a...

引用

作者： Tehrani, A.M. Kamel, M.S. Khamis, A.M. Pattern Analysis and Machine Intelligence Lab. Electrical and Computer Engineering Department University of Waterloo Waterloo Ont. N2L 3G1 Canada Mechanical Engineering Department University of Waterloo Canada Department of Cooperative Intelligent Systems Canada Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Canada Department of Systems Engineering and Automation Carlos III University of Madrid Spain

The work presented in this paper aims at combining fuzzy function approximation and reinforcement learning in order to create robotic soccer agents that are able to coordinate their behaviours locally and socially while learning from experience. This simultaneous coordination and learning ability can play a crucial role in improving the behaviour usage of robotic soccer agents. To achieve this goal, a fuzzy reinforcement learning technique for a single agent is first examined and then this technique is applied to multiple agents. The conducted experiments through a soccer simulation system show that the performance of robot scoring speed is improved using the proposed approach.

关键词： Multi agent systems

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An optimized two-step camera calibration method

An optimized two-step camera calibration method

引用

IEEE International Conference on Robotics and Automation (ICRA)

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

An optimized two-step camera calibration algorithm is developed. The proposed method starts with the well known linear calibration which approximates the transformation as a 3/spl times/4 matrix. Based on the results of the linear calibration and the camera model we construct the 4/spl times/4 homogeneous transformation matrix. Quaternion algebra is used to extract the optimum rotation matrix and this optimization is later extended to the other calibration parameters. Our calibration method includes nonlinear optimization which takes into consideration lens distortions. The convergence rate of the nonlinear optimization is accelerated by three more objective functions we introduced. To assess the accuracy of our proposed quaternion method, Euclidean norm of the error matrix between the original and computed homogeneous transformation matrices is calculated and compared to those of the existing methods. Simulations show that the quaternion method yields more accurate results both before and after the nonlinear optimization.

关键词： Optimization methods Cameras Calibration Quaternions Linear approximation Algebra Lenses Nonlinear distortion Convergence Acceleration

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An information theoretic image-quality measure

An information theoretic image-quality measure

引用

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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Image registration using virtual circles and edge direction

Image registration using virtual circles and edge direction

引用

作者： Alhichri, Haikel Salem Kamel, Mohamed Pattern Analysis and Machine Intelligence Lab. Systems Design Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada

In this paper, we combine a novel set of image features called virtual circles with edge direction, to provide an efficient alignment algorithm for image registration under similarity transformations. Virtual circles can be extracted efficiently from many types of images with the help of the distance transform. They can also have extra information, such as their radius and the dominant edge direction in neighborhood, which can be used for efficient registration. A heuristic called the smoothness criterion is used to choose virtual circles which are less likely to be corrupted by background noise, and to increases the efficiency of the algorithm. Experimental results have shown that this method is highly automatic and has linear complexity in terms of the number of virtual circles extracted. © 2002 IEEE.

关键词： Image segmentation

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Simulated Annealing with Opposite Neighbors

Simulated Annealing with Opposite Neighbors

引用

IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

This paper presents an improvement to the vanilla version of the simulated annealing algorithm by using opposite neighbors. This new technique, is based on the recently proposed idea of opposition based learning, as such our proposed algorithm is termed opposition-based simulated annealing (OSA). In this paper we provide a theoretical basis for the algorithm as well as its practical implementation. In order to examine the efficacy of the approach we compare the new algorithm to SA on six common real optimization problems. Our findings confirm the theoretical predictions as well as show a significant improvement in accuracy and convergence rate over traditional SA. We also provide experimental evidence for the use of opposite neighbors over purely random ones

关键词： Simulated annealing Convergence Computational intelligence Computational modeling pattern analysis Intelligent networks Competitive intelligence Laboratories System analysis and design design engineering

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Numerical condition of feedforward networks with opposite transfer functions

Numerical condition of feedforward networks with opposite tr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mario Ventresca Hamid Reza Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Numerical condition affects the learning speed and accuracy of most artificial neural network learning algorithms. In this paper, we examine the influence of opposite transfer functions on the conditioning of feedforward neural network architectures. The goal is not to discuss a new training algorithm nor error surface geometry, but rather to present characteristics of opposite transfer functions which can be useful for improving existing or to develop new algorithms. Our investigation examines two situations: (1) network initialization, and (2) early stages of the learning process. We provide theoretical motivation for the consideration of opposite transfer functions as a means to improve conditioning during these situations. These theoretical results are validated by experiments on a subset of common benchmark problems. Our results also reveal the potential for opposite transfer functions in other areas of, and related to neural networks.

关键词： Transfer functions Neurons Artificial neural networks Jacobian matrices Training Feedforward neural networks Erbium

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EVOL: ensembles voting online

EVOL: ensembles voting online

引用

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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A connectionist model for learning robotic grasps using reinforcement learning

A connectionist model for learning robotic grasps using rein...

引用

International Conference on Neural Networks

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

We present a connectionist model for learning robotic grasps. These grasps are represented as generic grasping functions. Our approach is to learn these functions by experimentation with the environment. Grasping rules are mapped to generic representations that can then be learned by experiments using neural networks. Furthermore, grasping rules acquired in this format can then be used on different objects using different grippers. During experimentation, reinforcement learning is used to minimize the number of failed experiments. Results show that the system is able to learn how to grasp various objects while maintaining a small number of experiments.

关键词： Learning Grippers Grasping Humans Shape Neural networks Robot sensing systems pattern analysis machine intelligence System analysis and design

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Improving the Convergence of Backpropagation by Opposite Transfer Functions

Improving the Convergence of Backpropagation by Opposite Tra...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Ventresca H.R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately learn the task is considerable. Many existing approaches have improved the convergence rate by altering the learning algorithm. We present a simple alternative approach inspired by opposition-based learning that simultaneously considers each network transfer function and its opposite. The effect is an improvement in convergence rate and over traditional backpropagation learning with momentum. We use four common benchmark problems to illustrate the improvement in convergence time.

关键词： Convergence Transfer functions Neural networks Backpropagation algorithms Genetic algorithms pattern analysis machine intelligence Laboratories System analysis and design design engineering

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