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A Novel Chinese Multi-Document Summarization Using Clustering Based Sentence Extraction

A Novel Chinese Multi-Document Summarization Using Clusterin...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： De-xi Liu Yan-xiang He Dong-hong Ji Hua Yang Hubei University of Arts and Science Xiangyang Hubei CN School of Computer Wuhan University Wuhan 430079 P. R. China Center for Study of Language and Information Wuhan University Wuhan 430079 P. R. China Center for Study of Language and Information Wuhan University of China Wuhan China Institute for Infocomm Research Singapore

This paper proposes a strategy for Chinese multi-document summarization based on clustering and sentence extraction. It adopts the term vector to represent the linguistic unit in Chinese document, which obtains higher representation quality than traditional word-based vector space model in a certain extent. As for clustering, we propose two heuristics to automatically detect the proper number of clusters: the first one makes full use of the summary length fixed by the user; the second is a stability method, which has been applied to other unsupervised learning problems. We also discuss a global searching method for sentence selection from the clusters. To evaluate our summarization strategy, an extrinsic evaluation method based on classification task is adopted. Experimental results on news document set show that the new strategy can significantly enhance the performance of Chinese multi-document summarization

关键词： Data mining Stability Helium Physics computing Natural languages Unsupervised learning Information retrieval Mathematical model Instruments Ontologies

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Fuzzy Matrix Computation for Fuzzy Information System to Reduce Attributes

Fuzzy Matrix Computation for Fuzzy Information System to Red...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Su-yun Zhao Eric C. C. Tsang Xi-zhao Wang De-gang Chen Daniel S. Yeung Department of computing Hong Kong Polytechnic University Hong Kong China Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding China Department of Mathematics and Physics North China Electric Power University Beijing China

Recently, many methods based on fuzzy rough sets are proposed to reduce fuzzy attributes. The common characteristic of these methods is that all of them are based on fuzzy equivalence relation. In other words, the underlying concept of rough sets, indispensability relation, is generalized to fuzzy equivalence relation. Here fuzzy equivalence relation is the binary relation, which is reflexive, symmetric and transitive. This paper tries to generalize the fuzzy equivalence relation to fuzzy similarity relation, which is more helpful to keeping the fuzzy information of initial data than fuzzy equivalence relation. Based on the fuzzy similarity relation, fuzzy matrix computation for information system is proposed which can be used to reduce fuzzy attributes. Firstly, fuzzy similarity relation who is isomorphic with the fuzzy similarity matrix is given as fuzzy indispensability relation. Then all the information of initial data, such as the similarity among objects and fuzzy inconsistence degree between two objects, can be represented by fuzzy similarity matrix. Secondly, by considering that the small perturbation of the fuzzy similarity matrix can be ignorable, we propose some basic concepts of knowledge reduction such as fuzzy attributes reduct, core and fuzzy significance of attributes etc in this paper. Thirdly, a heuristic algorithm based on the fuzzy significance of attributes is proposed to find close-to-minimal fuzzy attributes reduct. Finally, experimental comparisons with other methods of attributes reduction are given. The experimental results show that our method is feasible and effective

关键词： Fuzzy systems Information systems Fuzzy sets Rough sets machine learning Set theory Mathematics Physics computing Computer science Symmetric matrices

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MSBGA: A Multi-Document Summarization System Based on Genetic Algorithm

MSBGA: A Multi-Document Summarization System Based on Geneti...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Yan-xiang He De-xi Liu Dong-hong Ji Hua Yang Chong Teng School of Computer Wuhan University of China Wuhan China Center for Study of Language and Information Wuhan University of China Wuhan China School of Physics Xiangtan University Xiangfan China Institute for Infocomm Research Singapore School of Computer Wuhan University Wuhan 430079 P. R. China Center for Study of Language and Information Wuhan University Wuhan 430079 P. R. China

The multi-document summarizer using genetic algorithm-based sentence extraction (MSBGA) regards summarization process as an optimization problem where the optimal summary is chosen among a set of summaries formed by the conjunction of the original articles sentences. To solve the NP hard optimization problem, MSBGA adopts genetic algorithm, which can choose the optimal summary on global aspect. The evaluation function employs four features according to the criteria of a good summary: satisfied length, high coverage, high informativeness and low redundancy. To improve the accuracy of term frequency, MSBGA employs a novel method TFS, which takes word sense into account while calculating term frequency. The experiments on DUC04 data show that our strategy is effective and the ROUGE-1 score is only 0.55% lower than the best participant in DUC04

关键词： Genetic algorithms Data mining Frequency Clustering algorithms Fusion power generation Helium Physics computing Coherence Clustering methods Tides

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Power-Aware Hierarchical Scheduling with Respect to Resource Intermittence in Wireless Grids

Power-Aware Hierarchical Scheduling with Respect to Resource...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Chang-qin Huang Zhi-ting Zhu Yong-he Wu Zheng-hong Xiao ECNU-TCL Joint Workstation for Postdoctoral Research on Educational Technology East China Jiao Tong University Shanghai China College of Educational Information Technology South China Normal University Guangzhou China Department of Computer Science and Technology Hunan University of Arts and Sciences Changde China

Wireless grids bring more challenging issues of resource allocation and task scheduling, and mobility and power management are major additional concerns in a wireless grid. Based on a proxy-based architecture, a hierarchical scheduling model is proposed to efficiently utilize the energy of wireless nodes with respect to quality of service (QoS). The first level scheduler is responsible for mapping tasks among proxy-nodes and other fixed grid nodes; the second will conduct scheduling in each proxy-centric wireless domain. Under the guidance of four principles suitable for the intermittence, the first level performs overall scheduling based on the FIFS algorithm (first input first service). After modeling a consumed power objective function, a revised min-min heuristic algorithm is enforced to the second level in order to efficiently map tasks to wireless devices. In the latter algorithm, mobile node selection is targeted to minimize the energy consumed due to communication and computation, and its last solution is evaluated to guarantee requirement for the task deadline. This research simulation suggests that the power-aware hierarchical scheduling improves energy utilization of the overall system, and also decreases the ratio of failure of scheduled tasks in wireless grids

关键词： Resource management Processor scheduling Grid computing Energy management Mobile computing Batteries Educational technology Computer architecture Quality of service Mobile communication

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learning dynamic bayesian network with immune evolutionary algorithm

Learning dynamic bayesian network with immune evolutionary a...

引用

international conference on machine learning and Cybernetics, icmlc 2005

作者： Jia, Hai-Yang Liu, Da-You Yu, Peng Key Laboratory of Symbolic Computing and Knowledge Engineering Juin University Ministry of Education Changchun China

ISBN: (纸本)078039092X

Dynamic Bayesian Networks (DBNs) are directed graphical models of stochastic processes, How to learn the structure of DBNs from data is a hot problem of research. In this paper the author presents an Immune evolutionary algorithm for learning the network structure of DBNs. The results of contrast experiment prove that the constringency rate is more rapid than EGA-DBN algorithms. © 2005 IEEE.

关键词： learning systems

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Construction of low false alarm and high precision RBFNN for detecting flooding based denial of service attacks using stochastic sensitivity measure

Construction of low false alarm and high precision RBFNN for...

引用

international conference on machine learning and Cybernetics, icmlc 2005

作者： Ng, Wing W.Y. Chan, Aki P.F. Yeung, Daniel S. Tsang, Eric C.C. Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

ISBN: (纸本)078039092X

A good Intrusion Detection System (IDS) should have high precision on detecting attacks and low false alarm rates. machine learning techniques for IDS usually yield high false alarm rate. In this work, we propose to construct a host-based IDS for flooding-based Denial Of Service (DoS) attacks by minimizing the generalization error bound of the IDS to reduce its false alarm rate and increase its precision. Experiments using artificial datasets support our claims. © 2005 IEEE.

关键词： Radial basis function networks

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A covering model of granular computing

A covering model of granular computing

引用

international conference on machine learning and Cybernetics, icmlc 2005

作者： Ma, Jian-Min Zhang, Wen-Xiu Li, Tong-Jun Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049 China Information College Zhejiang Ocean University Zhoushan 316004 China

ISBN: (纸本)078039092X

This paper is mainly to study a covering model of granular computing in a set-theoretic setting. The model is based on the assumption that a finite set of universe is granulated through a family of overlapping granules based on a reflexive relation. Two operations, called zooming-in and zooming-out operations, are used to study connections between the elements of a universe and the elements of a granulated universe, as well as connections between computations in the two universes. © 2005 IEEE.

关键词： Set theory

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Estimation of intelligibility from received arbitrary speech signals with support vector machine

Estimation of intelligibility from received arbitrary speech...

引用

international conference on machine learning and Cybernetics, icmlc 2005

作者： Li, Francis F. Department of Computing and Mathematics Manchester Metropolitan University Manchester Ml 5GD United Kingdom

ISBN: (纸本)078039092X

Intelligibility, a vital concern of a speech transmission channel, is quantified using Speech Transmission Index (STI). The standard STI method relies on noisy test signals and thus hinders in-use measurements. Alternative methods to accurately estimate the STI from naturally occurring speech signals have been developed over the past few years using artificial neural networks. This paper presents a new machine learning based method to more accurately estimate the STI from arbitrary running speech using a purpose design signal pre-processor and support vector machines. When compared with the neural network approaches to the problem, the new method exhibits improved estimation accuracy and generalisation capability to arbitrary speech, providing a more applicable method to facilitate in-situ measurements. ©2005 IEEE.

关键词： Speech synthesis

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Structured large margin learning

Structured large margin learning

引用

international conference on machine learning and Cybernetics, icmlc 2005

作者： Wang, De-Feng Yeung, Daniel S. Ng, Wing W. Y. Tsang, Eric C. C. Wang, Xi-Zhao Department of Computing Hong Kong Polytechnic University Hung Horn Kowloon Hong Kong Faculty of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)078039092X

This paper presents a new large margin learning approach, namely structured large margin machine (SLMM), which incorporates both merits of "structured" learning models and advantages of large margin learning schemes. The promising features of this model, such as enhanced generalization ability, scalability, extensibility, and noise tolerance, are demonstrated theoretically and empirically. SLMM is of theoretical importance because it is a generalization of learning models like SVM, MPM, LDA, and M4 etc. Moreover, it provides a novel insight into the study of learning methods and forms a foundation for conceiving other "structured" classifiers. ©2005 IEEE.

关键词： learning systems

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Convergence of discrete Hopfield neural networks with multiple delays

Convergence of discrete Hopfield neural networks with multip...

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international conference on machine learning and Cybernetics, icmlc 2005

作者： Li, Xue-Gang Tsang, Eric C.C. Qiu, Shen-Shan Yang, Ming Dept. of Computing Hong Kong Polytechnic University HungHom Kowloon Hong Kong Hebei University Baoding China Shenzhen Polytechnic Shenzhen China

This paper investigates convergence theorems of (HNNMDs).(The discrete Hopfield network with multiple delays). We have demonstrated how to transform a network with multiple delays to a new network with a single delay.... 详细信息

ISBN: (纸本)078039092X

关键词： Neural networks

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