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Document classification using phrases

Joint IAPR 9th International Workshop on Structural and Syntactic pattern Recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in pattern Recognition, SPR 2002

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

ISBN: (纸本)3540440119

This paper presents a Bayes document classifier using phrases as *** e phrases are extracted using a grammar that iteratively applies the rules to the sequence of words in the document. This grammar is generated from a training set using statistical word association. W e report an improvement in the classification over the "bag of words" representation. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Classification (of information)

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Particle swarm clustering ensemble 08

Particle swarm clustering ensemble

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10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008

作者： Ahmadi, Abbas Karray, Fakhri Kamel, Mohamed Pattern Analysis and Machine Intelligence Lab. Department of Systems Design Engineering University of Waterloo 200 University Avenue West Waterloo ON Canada

ISBN: (纸本)9781605581309

Extracting natural groups of the unlabeled data is known as clustering. To improve the stability and robustness of the clustering outputs, clustering ensembles have emerged recently. In this paper, an ensemble of particle swarm clustering algorithms is proposed. That is, the members of the ensemble are based on the cooperative swarms clustering approaches. The performance of the proposed particle swarm clustering ensemble is evaluated using different data sets and is compared to that of other clustering techniques.

关键词： Particle swarm optimization (PSO)

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Extraction of text phrases using hierarchical grammar 15th

Extraction of text phrases using hierarchical grammar

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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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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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A reinfrocement learning approach to online learning in control

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

引用

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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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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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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Enhancement of vessel contours in retinal angiograms

Enhancement of vessel contours in retinal angiograms

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： N.M. Wanas M.R. El-Sakka M.S. Kamel Pattern Analysis and Machine Intelligence lab Department of Systems Design Engineerihg University of Waterloo Waterloo ONT Canada Department of Computer Science Faculty of Science University of Western Ontario London ONT Canada

Accurate and automatic assessment of angiograms has been sought as a powerful diagnostic tool in medical analysis, such as in diabetic retinopathy. These retinal angiogram images are characterized by poor local contrast. Applications of existing edge detection algorithms yield unsatisfactory results. In this paper, a set of cascaded linear directional filters is used to better enhance edges. Results show improvement in visual quality of the images.

关键词： Retina Image edge detection pattern analysis Diseases Blood vessels Biomedical imaging Image analysis Image segmentation machine intelligence System analysis and design

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A HYBRID MODEL FOR INTELLIGENT CONTROL-systems

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JOURNAL OF INTELLIGENT & ROBOTIC systems 1994年第3期10卷 221-241页

作者： AN, YF PANG, G APLEVICH, D 1. Pattern Analysis and Machine Intelligence Lab. System Design Engineering University of Waterloo N2L 3G1 Waterloo Ontario Canada 2. Department of Electrical and Computer Engineering University of Waterloo N2L 3G1 Waterloo Ontario Canada

Hybrid continuous-time and discrete-event system models are developed for applications to intelligent robot and autonomous control systems. Certain general properties, including the interface structure between the discrete event part and the continuous part of hybrid systems, are studied. Petri nets are proposed as the model for the discrete event sub-system (DES), and state equations with a set of binary parameters as the model for the continuous time sub-system (CTS) in a hybrid system. The quantized CTS is formulated in prime event structures, which can also be modeled by a condition/event Petri net. Therefore, a complete hybrid system can be fit into an extended Petri net formalism, which provides a unified framework for hybrid system description. The validity of the proposed model is demonstrated by modeling a two-tank system.

关键词： CONTINUOUS TIME systems DISCRETE EVENT systems HYBRID systems PETRI NETS MODELING INTELLIGENT CONTROL

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A fuzzy set approach to extracting keywords from abstracts

A fuzzy set approach to extracting keywords from abstracts

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： M. Makrehchi M. Kamel Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada Department of Systems Design Engineering University of Waterloo Waterloo Ontario Canada

An unsupervised keyword extraction method based on corpora is proposed. After representing each document in the collection by a fuzzy set of candidate keywords, the problem is translated into finding appropriate fuzzy membership degree for each candidate. In order to determine the membership degrees, first, all terms of the vocabulary are mapped into a two-dimensional space called class-collection map by obtaining two newly proposed fuzzy measures called fuzzy significance and fuzzy relevance for each term. At the second step, the mapped terms are grouped into three categories, namely, features, keywords and stopwords that are discriminated by their contribution to the meaning of the documents. Instead of a clustering approach for grouping purpose, a fuzzy rule base is provided. The method is independent of the language and data dimensionality. It does not require the use of dictionary, thesaurus nor natural language processing.

关键词： Fuzzy sets Vocabulary Web pages Abstracts Text mining Fuzzy systems pattern analysis machine intelligence System analysis and design design engineering

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