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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2835 条记录，以下是2421-2430 订阅

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Visualizing network status

Visualizing network status

引用

6th international conference on machine learning and Cybernetics

作者： Yoshida, Kenichi Shomura, Yusuke Watanabe, Yoshinori Univ Tsukuba Grad Sch Business Sci Tokyo 1120012 Japan Hitachi Ltd Cent Res Lab Tokyo 1858601 Japan ALAXALA Networks Corp Kawasaki Kanagawa 2120058 Japan

ISBN: (纸本)9781424409723

the recent evolution of various communication devices makes both network monitoring and configuration difficult. Although these network management tasks require deep understanding of network status, various new network devices make it difficult by increasing the complexity of whole network system. this paper proposes a visualization technique of network status that uses a frequent itemset mining algorithm to find important phenomena in the network. We also show that a simple interface with the proposed technique can visualize not only the ordinal network status but also the various security incidents. the understanding of the ordinal network status and the rapid finding of security incident helps the naive users in monitoring and configuring network equipments. Finding of various security incidents from real Internet traffic data are described as the typical usage of the proposed visualization technique.

关键词： network monitoring data mining

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A fast algorithm for constructing FP_TREE

A fast algorithm for constructing FP_TREE

引用

6th international conference on machine learning and Cybernetics

作者： Liu, Jiao-Min Guo, Sheng Wang, Zhen-Zhou Hebei Univ Sci & Technol Coll Informat Sci & Engn Shijiazhuang 050054 Peoples R China North China Elect Power Univ Coll Elect Engn Beijing 071003 Hebei Peoples R China

ISBN: (纸本)9781424409723

Recently, most of the studies on mining frequent patterns focus on improving the efficiency of frequent itemtsets generations, but the I/O cost of database scanning has been a bottle-neck problem in data mining. Many algorithms proposed recently are based on Apriori and FP_tree, and the FP_growth algorithm based on FP_tree is more efficient than Apriori because the candidates are not generated. But the construction of FP_tree may spend much time. therefore, the goal of our research is to propose a fast algorithm. In this paper, Level FP_tree that is constructed level by level (abbreviate LFP_tree) is proposed. the algorithm contains two main parts. the first is to scan the database only once for generating equivalence classes of each item. the second is to delete the non-frequent items and rewrite the equivalence classes of the frequent items, and then construct the LFP_tree. Experimental results have proved that LFP_tree is more efficient and scalable than FP_tree.

关键词： frequent pattern FP_growth FP_tree LFP_tree equivalence class

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A clustering algorithm for data mining based on swarm intelligence

A clustering algorithm for data mining based on swarm intell...

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6th international conference on machine learning and Cybernetics, ICMLC 2007

作者： Jin, Peng Zhu, Yun-Long Hu, Kun-Yuan Shenyang Institute of Automation The Chinese Academy of Sciences Shenyang 110016 China Graduate School Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)142440973X

Clustering analysis is an important function of data mining. Various clustering methods are need for different domains and applications. A clustering algorithm for data mining based on swarm intelligence called Ant-Cluster is proposed in this paper. Ant-Cluster algorithm introduces the concept of multi-population of ants with different speed, and adopts fixed moving times method to deal with outliers and locked ant problem. Finally, we experiment on a telecom company's customer data set with SWARM, agent-based model simulation software, which is integrated in SIMiner, a data mining software system developed by our own studies based on swarm intelligence. the results illuminate that Ant-Cluster algorithm can get clustering results effectively without giving the number of clusters and have better performance than k-means algorithm. © 2007 IEEE.

关键词： Clustering algorithms

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A fast determinate string matching algorithm for the network intrusion detection systems

A fast determinate string matching algorithm for the network...

引用

6th international conference on machine learning and Cybernetics

作者： Zhong, Cheng Chen, Guo-Liang Guangxi Univ Sch Comp & Elect & Informat Nanning 530004 Peoples R China

ISBN: (纸本)9781424409723

A perfect hash function for processing string is constructed by applying the Chinese Remainder theorem, and a fast string matching algorithm, which is suited to process the successive sequences like the network traffic data, is presented. the theoretical analysis shows that this algorithm not only obtains the determinate match results but also holds a linear time complexity in the worst case. the experiment results for matching a sequence database in the network intrusion detection systems also shows that this algorithm is efficient.

关键词： network intrusion detection stringmatching pattern matching Chinese remainder theorem

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Fundamental analysis of stock trading systems using classification techniques

Fundamental analysis of stock trading systems using classifi...

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6th international conference on machine learning and Cybernetics

作者： Cheng, Ching-Hsue Chen, You-Shyang Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu 640 Yunlin Taiwan

ISBN: (纸本)9781424409723

the traditional forecasting of revenue growth rate (RGR) is based on normal distribution. Due to emergence of information technology today, data mining has become one of important research trends. therefore, this paper mainly forecasts revenue growth rate of firms in stock trading systems by classification techniques. It is very important instrument for investors that correctly predict future growing firms from data of fundamental analysis in trading systems, because the accurate prediction of RGR will bring huge profit for investors in the future. this paper proposes a process to predict RGR of firms, which employs Decision tree C4.5, Bayes net, Multilayer perceptron and Rough sets techniques. Moreover, the paper uses the actual RGR dataset In Taiwan stock market to illustrate the proposed process. From the results, we recommend the rough set as analysis tool because the performance is superior to the listing methods and understandable rules are produced.

关键词： revenue growth rate data mining technique fundamental analysis

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machine-learning for dynamic reverse engineering of hedge funds

Machine-learning for dynamic reverse engineering of hedge fu...

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6th international conference on machine learning and Cybernetics

作者： Markov, Michael Muchnik, Ilya Mottl, Vadim Krasotkina, Olga Markov Proc Int 428 Springfield Ave Summit NJ 07901 USA Ruttgers Univ DIMACUS Piscataway NJ 08854 USA Russian Acad Sci Comput Ctr Moscow 119991 Russia Tula State Univ Tula Russia

ISBN: (纸本)9781424409723

the leave-one-out cross-validation in nested sets of data models is traditionally considered in machine learning as the basic instrument of finding the most appropriate subset of features or regressors in pattern recognition and regression estimation. We extend the notion of a nested set of models onto the problem of time-varying regression estimation, which implies, in addition to the generic challenge of choosing the subset of regressors, also the inevitable necessity to choose the appropriate level of model volatility, ranging from the full stationarity of instant models in time to their absolute independence of each other. So, there are, at least, two axes of model nesting in the problem of nonstationary regression estimation, first, the relevant size of the set of regressors and, second, the level of model volatility in time. We use the leave-one-out measure of the model fit as quality indicator along both nesting axes. We apply the proposed technique to analysis of a hedge fund's returns and reverse-engineering its strategies.

关键词： time-varying regression subset of regressors time volatility level leave-one-out procedure investment portfolio hedge fund dynamic style analysis

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Building knowledge base from semi-structured data

Building knowledge base from semi-structured data

引用

6th international conference on machine learning and Cybernetics

作者： Liu, Xiao-Li Wu, Guo-Qing Yang, Min Wuhan Univ Sch Comp Wuhan Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China ZTE Corp Shenzhen Peoples R China

ISBN: (纸本)9781424409723

Semantic pattern is kind of template with semantic information, and it can be used to structurize a sentence. In this paper, semantic pattern based question and its corresponding answers are used as the data resource of knowledge base. the exact answer is extracted from plain text answer by matching of question and answer. Two kinds of matching methods and the experiment results are simple presented. Extracted exact answer and semantic pattern based question are composed of a structured information unit which can be inserted into knowledge base with the assistance of ontology. the disease ontology is studied for illustration. Our approach to knowledge base construction is concise and effective.

关键词： knowledge extraction disease ontology semantic pattern

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A framework for agent-based distributed machine learning and data mining 07

A framework for agent-based distributed machine learning and...

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6th international Joint conference on Autonomous Agents and Multiagent Systems, AAMAS'07

作者： Tozicka, Jan Rovatsos, Michael Pechoucek, Michal Gerstner Laboratory Czech Technical University Technicka 2 Prague 166 27 Czech Republic School of Informatics University of Edinburgh Edinburgh EH8 9LE United Kingdom

ISBN: (纸本)9788190426275

this paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning success and learning progress by learning agents. We present an abstract architecture that enables agents to exchange models of their local learning processes and introduces a number of different methods for integrating these processes. this allows us to apply existing agent interaction mechanisms to distributed machine learning tasks, thus leveraging the powerful coordination methods available in agent-based computing, and enables agents to engage in meta-reasoning about their own learning decisions. We apply this architecture to a real-world distributed clustering application to illustrate how the conceptual framework can be used in practical systems in which different learners may be using different datasets, hypotheses and learning algorithms. We report on experimental results obtained using this system, review related work on the subject, and discuss potential future extensions to the framework. © 2007 IFAAMAS.

关键词： learning algorithms

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A fast method for determining the repeat pattern size in DNA sequences

A fast method for determining the repeat pattern size in DNA...

引用

6th international conference on machine learning and Cybernetics

作者： Zhou, Hong-Xia Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781424409723

Tandem repeats occur frequently in the human genome. the functions of them are still largely unclear, but some of them have been shown to cause human disease, and have relationship with regulatory functions. thus, detecting tandem repeats has considerable significance. Because of the undetermined length of repeat pattern and indels and substitutions existing in a tandem repeat, identifying a tandem repeat in genomic sequence data is a difficult task. In this paper, an efficient algorithm is proposed, which is based on the autoregressive (AR) model. We analyze residual errors of the AR model with different orders for a DNA sequence. According to changes of residual errors, we can determine whether a sequence contains a tandem repeat and what pattern size is. Examples show this algorithm can not only detect exact tandem repeats but also approximate ones.

关键词： autoregressive model tandem repeat pattern size residual error

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Reference knowledge service module designing for the graduation design

Reference knowledge service module designing for the graduat...

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6th international conference on machine learning and Cybernetics

作者： Wu, Yan-Wen Zhang, Kun-Ming Cent China Normal Univ Dept Informat & Technol Wuhan 430079 Peoples R China

ISBN: (纸本)9781424409723

During graduation design, the quality of the final dissertation is connected with the appropriate reference reading. therefore, the tutors should cost a great deal of time to choose reference for the students' research. the paper trial used to carry out the reference auto recommendation model within a graduation design intelligent tutor system according to the personalized recommendation technique which based on the user characteristic;it assisted to the teacher's reference offer work availably. though the test use, it preferably completed the auto recommendation of the student reference weekly, and the satisfaction of the teachers' and the students' is high.

关键词： personalized recommendation user interest model intelligent tutor system (ITS) graduation design data mining

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