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

共 80 条记录，以下是11-20 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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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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Opposite Transfer Functions and Backpropagation Through Time

Opposite Transfer Functions and Backpropagation Through Time

引用

IEEE Symposium on Foundations of Computational intelligence, FOCI

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

Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

关键词： Transfer functions Backpropagation algorithms Signal processing algorithms Recurrent neural networks Acceleration Convergence Computational intelligence Stability Nonlinear dynamical systems Computational complexity

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Opposition-Based Q(λ) with Non-Markovian Update

Opposition-Based Q(λ) with Non-Markovian Update

引用

IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, (ADPRL)

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

The OQ(λ) algorithm benefits from an extension of eligibility traces introduced as opposition trace. This new technique is a combination of the idea of opposition and eligibility traces to deal with large state space problems in reinforcement learning applications. In our previous works the comparison of the results of OQ(λ) and conventional Watkins' Q(λ) reflected a remarkable increase in performance for the OQ(λ) algorithm. However the Markovian update of opposition traces is an issue which is investigated in this paper. It has been assumed that the opposite state can be presented to the agent. This may limit the usability of the technique to deterministic environments. In order to relax this assumption the non-Markovian opposition-based Q(λ) (NOQ(λ)) is introduced in this work. The new method is a hybrid of Markovian update for eligibility traces and non-Markovian-based update for opposition traces. The experimental results show improvements of learning speed for the proposed technique compared to Q(λ) and OQ(λ). The new technique performs faster than OQ(λ) algorithm with the same success rate and can be employed for broader range of applications since it does not require determining state transition

关键词： Learning Usability Dynamic programming pattern analysis machine intelligence Laboratories System analysis and design design engineering systems engineering and theory State-space methods

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Search Difficulty of Two-Connected Ring-based Topological Network designs

Search Difficulty of Two-Connected Ring-based Topological Ne...

引用

IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Beatrice Ombuki-Berman Mario Ventresca Computer Science Department Brock University Saint Catharines ONT Canada Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Ring-based network design problems have many important applications, especially in the fields of logistics and telecommunications. This paper focuses on the recently proposed two-connected network with bounded rings problem. We investigate the search difficulty of both the Euclidean edge and unit edge length ring size bounds flavors of this problem by performing an information-theoretic fitness landscape analysis for several instances of the problem. Our results further confirm the hypothesis that the unit edge length version of the problem is indeed more difficult. The investigation also further reveals that smaller sized ring bounds lead to harder problems for Euclidean edge lengths. However, for unit edge lengths we did not establish such a relationship

关键词： Logistics Performance analysis Telecommunication network topology Costs Telecommunication traffic Computational intelligence Information analysis Helium Abstracts design optimization

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Increasing object recognition rate using reinforced segmentation

Increasing object recognition rate using reinforced segmenta...

引用

2006 IEEE International Conference on Image Processing, ICIP 2006

作者： Sahba, Farhang Tizhoosh, Hamid R. Salama, Magdy M. A. Systems Design Engineering Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ON Canada Electrical and Computer Engineering Pattern Analysis and Machine Intelligence Laboratory University of Waterloo 200 University Ave W Waterloo ON Canada

ISBN: (纸本)1424404819

In this paper a new approach to object extraction and recognition based on reinforcement learning is presented. We use this novel idea as a method to optimally segment the image and increase the recognition rate. The success rate is compared with a classical approach. Preliminary results demonstrate increase in recognition rate. ©2006 IEEE.

关键词： Image segmentation

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Improving the convergence of backpropagation by opposite transfer functions

Improving the convergence of backpropagation by opposite tra...

引用

IEEE International Joint Conference on Neural Network

作者： Ventresca, Mario Tizhoosh, Hamid R. Pattern Analysis and Machine Intelligence (PAMI) Laboratory Systems Design Engineering Department University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780780394902

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： Multilayer neural networks

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Opposition-based Q(λ) algorithm

Opposition-based Q(λ) algorithm

引用

International Joint Conference on Neural Networks 2006, IJCNN '06

作者： Shokri, Maryam Tizhoosh, Hamid R. Kamel, Mohamed Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo 200 University Avenue West ON N2L 3G1 Canada Pattern Analysis and Machine Intelligence Laboratory Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West ON N2L 3G1 Canada

ISBN: (纸本)0780394909

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 oppositionbased Q(λ). The results are compared with the conventional Watkins' Q(λ) and reflect a remarkable performance increase. © 2006 IEEE.

关键词： Reinforcement learning

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Modular-Based Classifier for Phoneme Recognition

Modular-Based Classifier for Phoneme Recognition

引用

IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

This paper proposes a modular-based classifier for the problem of phoneme recognition. This is carried out by the use of a two-level classification approach including, high and low levels. We propose a new concept called phoneme family. To obtain phoneme families, we employ k-mean clustering method. A given unknown phoneme is first classified into a phoneme family at high level classification. Then, the exact label of the phoneme is determined at low level classification. We have used a combined framework of statistical and neural network based classifiers. Encouraging results are obtained by applying the proposed method on TIMIT database and its performance is compared against other methods

关键词： Neural networks Speech recognition Recurrent neural networks pattern recognition design engineering Databases Network topology Signal processing Information technology pattern analysis

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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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