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Using Bayesian networks to model the belief in the opponent in static game with incomplete information

Using Bayesian networks to model the belief in the opponent ...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Xiao-Feng Wang Wei-Yi Liu Jin Li Yun Zhao Department of Computer Science Yunnan University China

ISBN: (纸本)0780384032

Noncooperative game theory provides a normative framework for analyzing strategic interactions of agents. In some noncooperative games agent may be lack of information about its opponents. So it must make decisions on uncertain opponents. In this paper, Bayesian network is used to model the agent uncertainty of its opponents. The uncertainty can be updated when some events happen through Bayesian network.

关键词： Intelligent networks Bayesian methods Game theory Computer science Probability distribution Light rail systems Information analysis Uncertainty Artificial intelligence Distributed computing

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A novelty Bayesian method for unsupervised learning of finite mixture models

A novelty Bayesian method for unsupervised learning of finit...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Hui Dai Wei-Min Ma School of Management Xi'an Jiaotong University Xi'an China School of Economics and Management Tsinghua University Beijing Beijing China

ISBN: (纸本)0780384032

Mixture models have universal applications in probabilistic modeling for multivariate data. This paper proposes a novelty parameters estimation and model selection method based on half Bayesian. In our algorithm, the mixture coefficient is a determinate variable, and the parameters of the components are random variables. Owing to the special prior distribution of the parameters, the parameters don't converge toward a singular estimation at the boundary of the parameter space, and the redundant components can be automatically removed. In a word, our algorithm has very good performances as follows: (1) automatically selects the number of components, (2) avoids converging the boundary of the parameter space, (3) is less sensitive to initialization, (4) can fulfil simultaneously the parameters estimation and model selection in one algorithm, therefore, the computation efficiency is higher. The experimental results show that the algorithm is effective and has above good performances.

关键词： Bayesian methods Unsupervised learning Parameter estimation Clustering algorithms machine learning algorithms Distributed computing Image analysis Stochastic processes Random variables High performance computing

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Using statistical analysis and support vector machine classification to detect complicated attacks

Using statistical analysis and support vector machine classi...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Ming Tian Song-Can Chen Yi Zhuang Jia Liu Department of Computing Institute of Information Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Yancheng Institute of Technology Yangcheng China

ISBN: (纸本)0780384032

Anomaly detection systems can detect unknown attacks, but they have a high false alarm rate. This article introduces our prototype that uses statistical analysis and support vector machine classifier to detect complicated attacks. We research the sampling methods of statistical analysis techniques, and propose a new statistical model named Smooth K-Windows. An improved support vector machine classifier that has higher accuracy is proposed after analyzing the reason why support vector machine makes misclassifications. The experimental results show that the prototype system can detect complicated attacks in which the attackers stash their behavior by changing it gradually.

关键词： Statistical analysis Support vector machine classification Support vector machines Prototypes Intrusion detection Sampling methods learning systems machine learning Space technology Electronic mail

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Research on algorithm of user query frequent itemsets mining

Research on algorithm of user query frequent itemsets mining

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Yong Zhong Xiao-Lin Qin Information Science and Technology Institute Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)0780384032

An algorithm of mining database user query profiles of transaction level is presented. The algorithm changes the computing method of the support and confidence in association rules mining by adding query structure and attribute relations to the computation. Since there is no causal relationship in the access of attributes in queries, the method is more appropriate to describe user query behaviors than itemsets used by association rules. The method can be used in database intrusion detection system to prevent database from illegal intrusions effectively. Realization, performance and application of the algorithm are discussed in the paper.

关键词： Itemsets Data mining Intrusion detection Association rules Transaction databases Data security machine learning algorithms Information science Space technology Power system security

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Fuzzy cognitive map learning based on improved nonlinear Hebbian rule

Fuzzy cognitive map learning based on improved nonlinear Heb...

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international conference on machine learning and Cybernetics (icmlc)

作者： Sheng-Jun Li Rui-Min Shen Computer Science Department Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780384032

Fuzzy cognitive map (FCM) is a powerful soft computing technique for modeling complex systems. It is a combination of fuzzy logic theory and neural networks. Developing of FCM is easy and adaptable based on human knowledge and experience. On the other hand, the main dependence on experts' knowledge and opinion, and the potential convergence to undesire steady states are the shortcomings of FCMs. learning methods are good choices used to overcome the shortcomings and strengthen the efficiency and robustness of FCM. This paper proposes one improved Hebbian algorithm on non-linear units for training FCMs. With the proposed learning procedure, FCM can modify its fuzzy causal web as casual pattern change and update their causal knowledge as experts.

关键词： Fuzzy cognitive maps Fuzzy systems Power system modeling Fuzzy logic Neural networks Humans Convergence Steady-state learning systems Robustness

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Parallel computing for the longest common subsequences in network intrusion detection system

Parallel computing for the longest common subsequences in ne...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Cheng Zhong Guo-Ling Chen Jia-Hua He Department of Computer Science and Technology University of Science and Technology Hefei China School of Computer and Electronics and Information Guangxi University Nanning China

ISBN: (纸本)0780384032

The longest common subsequence problem has been applied to network instruction detection system, bioinformatics and e-commerce, etc. This paper proposes an extended longest common subsequence problem called (K,1)-LCS problem, designs a parallel algorithm to solve (K,1)-LCS problem on SMP machine by the divide and conquer strategy and the tournament tree, and then presents a parallel algorithm for solving (K,1)-LCS problem on SMP clusters by applying the k-selection technique based on mesh-connected network. The theoretical analysis and experiments on Dawning-2000 parallel computer show that the parallel algorithm obtains a linear speedup and has a very good scalability.

关键词： Parallel processing Intelligent networks Intrusion detection Parallel algorithms Concurrent computing Algorithm design and analysis Clustering algorithms Phase change random access memory Helium Computer aided instruction

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Development of a Java-based distributed platform for the implementation of computation intelligence techniques

Development of a Java-based distributed platform for the imp...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Chun-Che Fung Jia-Bin Li Kit Po Wong School of Information Technology Murdoch University Murdoch WA Australia Department of Electrical Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780384032

This work reports three phases of development of a Java-based distributed system for the implementation of computational intelligence techniques to solve practical engineering problems. The first design of the Java-based system is called Para Worker. It is a library of application program interface (APIs) to facilitate parallel computation. Next, a new system, termed Para Worker 2 aimed at improving ease of programming and performance. It also adds enhanced features of improved dynamic object reallocation, adaptive consistency protocols, and location transparency. However, neither of these systems addressed the fundamental issues of Java virtual machine (JVM) in distributed environment. The third phase of development of the Java-based system is a new form of distributed JVM in order to execute Java program efficiently. The new system is termed Java distributed platform (JavaDP).

关键词： Java Distributed computing Competitive intelligence Concurrent computing Power engineering computing Throughput Performance evaluation Artificial neural networks Fuzzy logic Power engineering and energy

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Integrating dependency structures of multi Bayesian networks

Integrating dependency structures of multi Bayesian networks

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international conference on machine learning and Cybernetics (icmlc)

作者： Wei-Hua Li Wei-Yi Liu Zhong-Yu Zhang The Department of Compute Science Yunnan University Kunming Yunnan China The Department of physics Science Yunnan Qujing normal college China

ISBN: (纸本)0780384032

Bayesian network is a powerful common knowledge representation and reasoning tool for partial beliefs under uncertainty. However, knowledge-based system sometimes must be able to "intelligently" manage a large amount of information coming from different sources and at different moments in time. From the point of view of graph, we suggest a more intuitive algorithm that combines multi Bayesian networks and constructs the dependency structure preserving as much information as possible.

关键词： Bayesian methods Markov random fields Knowledge representation Uncertainty Graph theory Data structures Boolean functions Computer networks Physics computing Educational institutions

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Sensitivity analysis of prior knowledge in knowledge-based neurocomputing

Sensitivity analysis of prior knowledge in knowledge-based n...

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international conference on machine learning and Cybernetics (icmlc)

作者： I. Cloete S. Snyders D.S. Yeung X. Wang International University Bremen Germany Department of Computing Hong Kong Polytechnic University Hong Kong China

Knowledge-based neurocomputing addresses, among other things, the encoding and refinement of symbolic knowledge in a neurocomputing paradigm. Prior symbolic knowledge derived outside of neural networks can be encoded in neural network form, and then further trained. Previous research suggested certain values for the weights that represent prior knowledge, based on an analysis of the derivative of the error function. This inductive bias is investigated empirically, and furthermore, we show how to use sensitivity analysis methods to investigate this bias. This work shows that the bias of the encoding method for the prior knowledge corresponds well with a range of good parameter values that retain the encoded knowledge and allows refinement by further training.

关键词： Sensitivity analysis Neural networks Neurons Encoding Computer networks Electronic mail Vents machine learning Transfer functions

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Parallel implementation of CLARANS using PVM

Parallel implementation of CLARANS using PVM

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Ya-Ping Zhang Ji-Zhao Sun Yi Zhang Xu Zhang School of Electronic Information Engineering Tianjin University TJU Tianjin China

ISBN: (纸本)0780384032

An attempt of employing network resources to solve a complex and time-consuming data mining problem is presented. The CLARANS is selected as the study objective. An improved CLARANS algorithm is first developed, in which the more inherent concurrency is explored. Then its parallel implementation by using PVM mechanism and the running performance analysis are provided. The analysis results show the expected speed-up is obtained and demonstrate that some parallel data mining algorithm is more effective in a distributed network.

关键词： Clustering algorithms Data mining Concurrent computing Performance analysis Collaborative work Robustness Parallel processing Sun Data engineering Electronic mail

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