7th International Conference on Hybrid Intelligent Systems (HIS 2007)最新文献_第5页

Advanced Learning of SOR Network Employing Evaluation-based Topology Representing Network 基于评估的拓扑表示网络的SOR网络高级学习

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.72

T. Yamakawa, K. Horio, Takahiro Tanaka

{"title":"Advanced Learning of SOR Network Employing Evaluation-based Topology Representing Network","authors":"T. Yamakawa, K. Horio, Takahiro Tanaka","doi":"10.1109/HIS.2007.72","DOIUrl":"https://doi.org/10.1109/HIS.2007.72","url":null,"abstract":"Learning systems such as multi-layer feed-forward neural networks, wavelet networks and so on need appropriate learning data (input data and teaching output data). These methods are not so useful in case when we cannot get the appropriate learning data. Even in this case, it is not so difficult to evaluate the system output for arbitrarily applied input. The learning data of input-output pairs with their evaluations are easily obtained and thus is easily used for modeling the system. SOR (self-organizing relationship) network is a modeling tool, which can be established by a set of input-output data and corresponding evaluation. This SOR network can act as a knowledge acquisition system and also act as a fuzzy inference engine. The linkage among the units in competitive layer is fixed and not flexible, and thus not used for complicated systems. In this plenary talk, the advanced learning process is presented for the original SOR network by employing evaluation-based TRN (topology representing network). By this learning, the linkage among the units in the competitive layer can be more flexible and thus used for modeling of much more complicated systems. The application of the SOR network established by this learning process to a manipulation control is also presented.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127145534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Trade-off Exploration by Query-Trail-Mediated Topology Reconstruction in Unstructured P2P Networks 基于查询轨迹中介拓扑重构的非结构化P2P网络性能权衡探索

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.22

K. Ohnishi, S. Nagamatsu, Y. Oie

引用次数: 1

Computing Sharp Lower and Upper Bounds for the Minimum Latency Problem 计算最小延迟问题的明显上下边界

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.39

J. Sarubbi, H. Luna, G. J. Miranda, R. Camargo

引用次数: 0

Symbiotic Tabu Search, A General Evolutionary Optimization Approach 共生禁忌搜索，一种通用的进化优化方法

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.70

R. Halavati, S. Shouraki, Bahareh Jafari Jashmi, M. Heravi

引用次数: 1

Generating Fuzzy Rules from Examples Using the Particle Swarm Optimization Algorithm 基于粒子群优化算法的实例模糊规则生成

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.52

A. Esmin

引用次数: 34

Implementation and Experimental Evaluation of On-Line Simulation Server for OSPF-TE OSPF-TE在线仿真服务器的实现与实验评价

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.42

H. Tamura, Tsuyoshi Okubo, Yousuke Inoue, K. Kawahara, Y. Oie

{"title":"Implementation and Experimental Evaluation of On-Line Simulation Server for OSPF-TE","authors":"H. Tamura, Tsuyoshi Okubo, Yousuke Inoue, K. Kawahara, Y. Oie","doi":"10.1109/HIS.2007.42","DOIUrl":"https://doi.org/10.1109/HIS.2007.42","url":null,"abstract":"As the amount of traffic transferee! on the Internet are increasing, dynamic traffic engineering (TE) becomes important to avoid link congestion. In open shortest path fast(OSPF)-based networks, link costs are statically set according to its long-term utilization for reducing traffic on some congested nodes, hence temporary performance degradation may occur due to short-term traffic fluctuation. For dynamic IE on OSPF-based networks, measurement of the utilization of links / nodes, inferring the set of link costs for improving transmission behavior and setting the cost set to routers are necessary and so-called on-line simulation (OLS) system can operate these functions autonomously and periodically. In this paper, we construct the server prototype in OLS system, and evaluate its scalability and control performance in our testbed network. Experimental results show that the server succeeds in providing low-cost network management and real-time control even if there is the large amount of traffic on the network. Furthermore, the total throughput over the network was greatly improved by the OLS.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126631160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning to Reach Optimal Equilibrium by Influence of Other Agents Opinion 学习在其他主体意见的影响下达到最优均衡

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.61

D. Barrios-Aranibar, L. Gonçalves

引用次数: 3

Evolving Connectionist and Hybrid Systems: Methods, Tools, Applications 不断发展的联结主义和混合系统:方法、工具和应用

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.74

N. Kasabov

引用次数: 0

A Multi-Objective Genetic Algorithm for Discovering Non-Dominated Motifs in DNA Sequences DNA序列非支配基序发现的多目标遗传算法

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.65

Mehmet Kaya

{"title":"A Multi-Objective Genetic Algorithm for Discovering Non-Dominated Motifs in DNA Sequences","authors":"Mehmet Kaya","doi":"10.1109/HIS.2007.65","DOIUrl":"https://doi.org/10.1109/HIS.2007.65","url":null,"abstract":"This paper presents a novel motif discovery algorithm based on multi-objective genetic algorithms to extract non-dominated motifs in DNA sequences. The main advantage of our approach is that a large number of tradeoff (non-dominated) motifs can be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization. In this paper, the method extracts non-dominated motifs taking into account two-objective at a time while one of the objectives is set to a pre-specified value. So, user is given to the authority of incorporating to motif discovery process. Our approach can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the discovered non-dominated motifs, the decision maker can understand the tradeoff between the objectives. We compare the approach with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime and accuracy of prediction.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116174136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Pareto-based Multi-Objective Machine Learning 基于pareto的多目标机器学习

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.73

Yaochu Jin

{"title":"Pareto-based Multi-Objective Machine Learning","authors":"Yaochu Jin","doi":"10.1109/HIS.2007.73","DOIUrl":"https://doi.org/10.1109/HIS.2007.73","url":null,"abstract":"Machine learning is inherently a multi-objective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggregated to a scalar cost function. This can mainly attributed to the fact that most conventional learning algorithms can only deal with a scalar cost function. Over the last decade, efforts on solving machine learning problems using the Pareto-based multi-objective optimization methodology have gained increasing impetus, particularly thanks to the great success of multi-objective optimization using evolutionary algorithms and other population-based stochastic search methods. It has been shown that Pareto-based multi-objective learning approaches are more powerful compared to learning algorithms with a scalar cost functions in addressing various topics of machine learning, such as clustering, feature selection, improvement of generalization ability, knowledge extraction, and ensemble generation. This talk provides first a brief overview of Pareto-based multi-objective machine learning techniques. In addition, a number of case studies are provided to illustrate the major benefits of the Pareto-based approach to machine learning, e.g., how to identify interpretable models and models that can generalize on unseen data from the obtained Pareto-optimal solutions. Three approaches to Pareto-based multi-objective ensemble generation are compared and discussed in detail. Most recent results on multi-objective optimization of spiking neural networks will be presented.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114669384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32