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15th Conference of the Canadian Society for Computational Studies of intelligence, AI 2002

作者： Bakus, Jan Kamel, Mohamed Carey, Tom Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada Department of Management Sciences University of Waterloo WaterlooON Canada

ISBN: (纸本)9783540479222

This paper presents an algorithm for extraction of phrases from text *** e algorithm builds phrases by iteratively merging bigrams according to an association *** o association measures are presented: mutual information and t-test.T he extracted phrases are tested in a document classification task using a tf/idf model and a k-nearest neighbor classifier. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Extraction

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Representing Discovered patterns Using Attributed Hypergraph 2

Representing Discovered Patterns Using Attributed Hypergraph

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2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

作者： Wang, Yang Wong, Andrew K.C. Department of Systems Design Engineering Pattern Analysis and Machine Intelligence Group University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)1577350049

One of the fundamental problems in knowledge discovery in databases and other applications of AI is how to represent knowledge and *** representation schemes have various *** this paper, we propose a new knowledge representation scheme using attributed hypergraph (AHG), which is simple yet general enough to directly encode different order patterns discovered from large *** AHG, both the qualitative and quantitative relations are represented as attributed *** representation is lucid and transparent for ***, patterns in AHG are easy to *** the discussion, some basic manipulations of AHG for data mining tasks are briefly *** paper ends with examples of pattern representation using AHG. © 1996 AAAI (***). All Rights Reserved.

关键词： Knowledge representation

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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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Opposition-based Q(λ) algorithm

Opposition-based Q(λ) algorithm

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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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A reinfrocement learning approach to online learning in control

A reinfrocement learning approach to online learning in cont...

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Fourth International Conference on Control and Automation

作者： Tehrani, Ali M. Kamel, Mohamed S. Pattern Analysis and Machine Intelligence Lab Systems Design Engineering Department University of Waterloo Waterloo Ont. N2L 3G1 Canada

ISBN: (纸本)078037777X

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. © 2003 IEEE.

关键词： Learning systems

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Intelligent control of an autonomous mobile robot in a hazardous material spill accident ȁ4;a blackboard structure approach

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Robotics and Autonomous systems 1990年第4期6卷 351-365页

作者： Pang, Grantham K.H. Shen, Helen C. Department of Electrical and Computer Engineering University of Waterloo WaterlooOntarioN2L 3G1 Canada Pattern Analysis and Machine Intelligence Group Department of Systems Design Engineering University of Waterloo WaterlooOntarioN2L 3G1 Canada

This paper describes the use of the blackboard architecture for the high level programming and control of autonomous mobile robots in a hazardous material spill emergency situation. The purpose of the robot is to perform some of the tasks which are dangerous for the emergency response personnels. The proposed blackboard system provides a unifying framework for the integration of information from various sensor-based and knowledge-based sub-systems. © 1990 Elsevier Science Publishers B.V. (North-Holland) All rights reserved

关键词： Hazardous materials spills

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Modular-Based Classifier for Phoneme Recognition

Modular-Based Classifier for Phoneme Recognition

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

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

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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Multiple Cooperating Swarms for Data Clustering

Multiple Cooperating Swarms for Data Clustering

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IEEE Swarm intelligence Symposium, SIS

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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Data dependence in combining classifiers

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4th International Workshop on Multiple Classifier systems, MCS 2003

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

ISBN: (纸本)3540403698

It has been accepted that multiple classifier systems provide a platform for not only performance improvement, but more efficient and robust pattern classification systems. A variety of combining methods have been proposed in the literature and some work has focused on comparing and categorizing these approaches. In this paper we present a new categorization of these combining schemes based on their dependence on the data patterns being classified. Combining methods can be totally independent from the data, or they can be implicitly or explicitly dependent on the data. It is argued that data dependent, and especially explicitly data dependent, approaches represent the highest potential for improved performance. On the basis of this categorization, an architecture for explicit data dependent combining methods is discussed. Experimental results to illustrate the comparative performance of some combining methods according to this categorization is included. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Computers

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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...

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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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