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Feature subset selection for support vector machines through sensitivity analysis

Feature subset selection for support vector machines through...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： De-Feng Wang P.P.K. Chan D.S. Yeung E.C.C. Tsang Department of computing Hong Kong Polytechnic University Kowloon Hong Kong China

In the context of support vector machines, feature selection is motivated mainly by the consideration of classification speed and generalization ability. Sensitivity analysis of MLP and RBF has already been successfully applied in feature subset selection. We present a novel feature selection method for support vector machines (SVMs) using the sensitivity analysis of SVMs, which is defined as the deviation of separation margin with respect to the perturbation of given feature. The method we proposed can directly be applied to multi-class SVMs. Our experiments validate that the proposed strategy produces satisfactory results both on artificial and real-world data.

关键词： Support vector machines Sensitivity analysis Support vector machine classification Filters machine learning Electronic mail Text processing Internet Gene expression Input variables

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Comparison of parallel and cascade methods for training support vector machines on large-scale problems

Comparison of parallel and cascade methods for training supp...

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international conference on machine learning and Cybernetics (icmlc)

作者： Bao-Liang Lu Kai-An Wang Yi-Min Wen Department of Computer Science & Engineering Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780384032

We have proposed two different methods for training support vector machines (SVMs) on large-scale pattern classification problems, namely min-max-modular SVM (M3-SVM) and cascade SVM (C-SVM). For speeding up the training of SVMs with new computing infrastructure such as cluster and grid systems, both methods decompose a large-scale two-class problem to a number of relatively smaller two-class sub-problems which can be implemented in a parallel way, but they use different decomposition and combination strategies. In this paper, we conduct a comprehensive investigation in the two methods to compare their generalization performance and training time. Our experiments show that M3-SVM needs shorter training time, but has a little lower generalization performance than the standard SVM and cascade SVM. The experiments also indicate that cascade SVM has the least number of support vectors among these three SVMs.

关键词： Support vector machines Support vector machine classification Large-scale systems Industrial training Text categorization Training data Quadratic programming Pattern classification Concurrent computing Grid computing

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Reinforcement learning method based on semi autonomous agent in combatant searching safe blindage

Reinforcement learning method based on semi autonomous agent...

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international conference on machine learning and Cybernetics (icmlc)

作者： Ke-Wei Yang Yue-Jin Tan Institute of System Engineering School of Humanities and Management National University of Defense Technology Changsha China

ISBN: (纸本)0780384032

learning is an essential capability which an intelligent agent can own. We exert the profit-sharing reinforcement learning method into the semi-autonomous agent system (SAS). The semi-autonomous agent has constrained autonomy, and we call it restriction property which makes the SAS more flexible and robust in the military simulation and ITS etc. Profit-sharing method is more robust and fit for the dynamic environment which includes much uncertain factors, especially in the partial MDPs (Markov decision processes) environment, such as the battlefield. We propose an improving reinforcement learning method of profit-sharing used in the SAS. The new algorithm has its roots in the trait of SAS and the merit of profit-sharing reinforcement learning method. At last a trial of combatant searching safe blindage is introduced to prove the effectiveness of such approving profit-sharing method in the SAS.

关键词： Autonomous agents Synthetic aperture sonar Robustness Intelligent agent Military computing Uncertainty machine learning Systems engineering and theory Technology management Engineering management

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An extended Isomap algorithm for learning multi-class manifold

An extended Isomap algorithm for learning multi-class manifo...

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international conference on machine learning and Cybernetics (icmlc)

作者： Yiming Wu Kap Luk Chan School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)0780384032

Isomap is a recently proposed algorithm for manifold learning and nonlinear dimensionality reduction. In the Isomap algorithm, geodesic distances between points are extracted instead of simply taking the Euclidean distance, thus a geometric distance graph is constructed and the embedding is obtained from the graph. However, when this method is applied into multi-class data, several isolated sub-graphs will form thus desirable embedding cannot be achieved. An extended Isomap algorithm is proposed for the multi-class manifold learning which computes within-class and between-class geodesic distances separately and the final embedding is obtained from the augmented geodesic distance matrix using multidimensional scaling algorithm. Experimental results on synthetic and real data reveal the promising performance of the proposed method.

关键词： Manifolds Principal component analysis Cameras Multidimensional systems Stress measurement Geophysics computing Nearest neighbor searches Laplace equations Linear approximation Sparse matrices

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A scalable object-based intelligent storage device

A scalable object-based intelligent storage device

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international conference on machine learning and Cybernetics (icmlc)

作者： D. Feng Ling-Jun Qin Ling-Fang Zeng Qun Liu Key Laboratory of Data Storage System Ministry of Education School of Computer Huazhong University of Science and Technology Wuhan China

A scalable object-based intelligent storage device (SOID) is presented in this paper. A scalable way of describing the object attributes of SOID is discussed and is adaptive to many application fields. In the SOID system, the users can register and upload methods at the device and associate an object with a chain of methods. Any kind of operations on objects can be performed by the method chains which are executed by taking the object as an input stream in a SOID. The SOID has also explored two promising enhancements: object-based interface and embedded computational capabilities. It improves the scalability of storage system.

关键词： Computational intelligence Computer architecture Streaming media Computer interfaces Embedded computing Intelligent networks Laboratories Data storage systems Computer science education Educational technology

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Mind evolutionary computation with swarm intelligence

Mind evolutionary computation with swarm intelligence

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international conference on machine learning and Cybernetics (icmlc)

作者： Gang-Zhu Qiao Jing Jie Jian-Chao Zeng Xiao-Juan He Division of System Simulation and Computer Application Taipan Heavy Machinery Institute Taiyuan China

ISBN: (纸本)0780384032

Mind evolutionary computation (MEC) is a novel stochastic algorithm that derived from man's swarm intelligence. Based on the swarm theory, the social behavior analysis about MEC is made and the searching mechanisms of its operators are studied. After that, a parameter analysis is provided for the similar axis operator, and a cooperation-based dissimilation operator (CDO) is developed. Finally, a series of experiments have been done to make a parameter choice and an evaluation for MEC. The results illustrate MEC with CDO is a viable global optimization method owning robust ability.

关键词： Evolutionary computation Particle swarm optimization Computational modeling Optimization methods Biological system modeling Evolution (biology) Analytical models Algorithm design and analysis Robustness Biology computing

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A novel distributed cooperative fault-tolerance storage mechanism

A novel distributed cooperative fault-tolerance storage mech...

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international conference on machine learning and Cybernetics (icmlc)

作者： Xu Zhou Xian-Liang Lu Meng-Shu Hou Chuan Zhan School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (纸本)0780384032

This paper presents a novel distributed cooperative fault-tolerance storage mechanism (DCFS) based on file replication and XOR verification. By using XOR verification files, DCFS makes a group of files, which are distributed among different hosts cooperative, so that not only a single file in the group can use its XOR verification files to improve its own availability, but also the availability of the group can be improved greatly. Using the graph description of DCFS, we present the principle of DCFS and give a quantitative analysis to its performance. Experimental results have provided evidence of high fault-tolerance of DCFS.

关键词： Fault tolerance Availability File systems Tiles Fault tolerant systems Computer science Performance analysis Graph theory Computer networks Distributed computing

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A fuzzy interpolative reasoning method

A fuzzy interpolative reasoning method

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international conference on machine learning and Cybernetics (icmlc)

作者： Dong-Mei Huang E.C.C. Tsang D.S. Yeung College of Science Hebei Agricultural University Baoding China Department of Computing Hong Kong Polytechnic University Hong Kong China

Interpolative reasoning methods is a reasoning technique that is designed to deal with reasoning in sparse rule-based systems. This paper proposed a fuzzy interpolative reasoning method by using a like - gravity - center of fuzzy sets whose shapes are trapezoidal. This method allows the conditions appearing in the antecedent part and the consequence of the rules to be represented by trapezoidal fuzzy numbers. The work steps of the presented method are first constructing a new inference rule by manipulating two given adjacent rules and next by exploiting similarity information to convert the derived inference result into the conclusion. In this process, we use scale and move rate transformation operation to support such reasoning.

关键词： Fuzzy reasoning Fuzzy sets Interpolation Artificial intelligence Gravity Fuzzy systems Agriculture Shape Bismuth Euclidean distance

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Online autonomous auction model based on agent

Online autonomous auction model based on agent

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international conference on machine learning and Cybernetics (icmlc)

作者： Jing Huang Da-You Liu Bo Yang College of Computer Science and Technology Jilin University ChangChun China

ISBN: (纸本)0780384032

Auction is a typical commercial activity. Online autonomous auction based on agent is a rising research field in electronic commerce and has a broad application foreground. Most researches focus on the mechanisms and strategies of auction but neglect the study of other key issues in building online autonomous auction system, such as locating the auction hall and the cooperation between trade partners. So the paper investigates these questions based on mobile agent technology and game theory. In the paper an autonomous online auction model is brought forward and implemented, which supports two auction mechanisms of first price sealed-bid auction and double auction, and the linear bidding strategy of the two auction mechanisms is also given.

关键词： Internet Electronic commerce Mobile agents Business Grid computing Educational institutions Computer science Application software Paper technology Game theory

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The geometric constraint solving based on memory particle swarm algorithm

The geometric constraint solving based on memory particle sw...

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international conference on machine learning and Cybernetics (icmlc)

作者： Chun-Hong Cao Wen-Hui Li Yong-Jian Zhang Rong-Qing Yi College of computer science and technology Jilin University Changchun China Department of Information and Electronical Engineering Shandong Institute of Architecture and Engineering Jinan China

ISBN: (纸本)0780384032

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. PSO is an evolution computing method. It searches the solution space by creating a better next swarm. The new swarm is produced based on the new individuals. Memory particle swarm algorithm is a PSO algorithm that adds a memory influence. We introduce MPSO into geometric constraint solving. The purpose of the added memory feature is to maintain spread and therefore diversity by providing individual specific alternate target points to be used at times instead of the current local best position. The experiment indicates that the algorithm is effective.

关键词： Particle swarm optimization Nonlinear equations Constraint optimization Educational institutions Computer science Computer architecture Stability Matrix decomposition machine learning

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