7th International Conference on Hybrid Intelligent Systems (HIS 2007)最新文献_第4页

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.19

C. Almeida, R. Souza, C. Rodrigues, N. L. C. Junior

引用次数: 1

Global Modes in Kernel Density Estimation: RAST Clustering 核密度估计的全局模式:RAST聚类

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.32

O. Wirjadi, T. Breuel

引用次数: 1

On the Use of the SVM Approach in Analyzing an Electronic Nose SVM方法在电子鼻分析中的应用

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.16

M. Gaudioso, Walaa Khalaf, C. Pace

引用次数: 16

A Hybrid Texture Analysis System based on Non-Linear & Oriented Kernels, Particle Swarm Optimization, and kNN vs. Support Vector Machines 基于非线性和定向核、粒子群优化和kNN与支持向量机的混合纹理分析系统

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.18

Stefanie Peters, A. König

引用次数: 8

Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation 进化随机森林生成的杂交群元启发式算法

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.9

M. Bursa, L. Lhotská, M. Macas

引用次数: 21

Preference Articulation in Evolutionary Multiobjective Optimisation 进化多目标优化中的偏好衔接

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.75

C. Fonseca

{"title":"Preference Articulation in Evolutionary Multiobjective Optimisation","authors":"C. Fonseca","doi":"10.1109/HIS.2007.75","DOIUrl":"https://doi.org/10.1109/HIS.2007.75","url":null,"abstract":"Real-world optimisation problems often involve a number of conflicting criteria, or objectives. Such problems usually admit multiple Pareto-optimal solutions, i.e. solutions, which cannot be improved upon in all objectives simultaneously. In practice, however, acceptable solutions must perform sufficiently well with respect to all objectives, which means that not all Pareto-optimal solutions may be satisfactory. Evolutionary approaches to multiobjective optimisation have concentrated mainly on the task of approximating the set of Pareto-optimal solutions of a given problem as well as possible, by generating diverse sets of non-dominated alternatives. Subjective information concerning how different combinations of objective values influence the relative quality of a solution is not required, but this approach tends to become impractical as the number of objectives grows. In practice, however, there are many situations in which such preference information is either available a priori or may be acquired during the initial steps of an optimisation run, even if not in a complete form. Incorporating preference information in evolutionary multiobjective optimisation (EMO) algorithms allows the search to concentrate on, and to better approximate, the relevant regions of the Pareto-optimal front. In this talk, a number of ways in which preference information may be combined with evolutionary search, in order to improve the relevance and the quality of the optimisation results will be discussed, and application examples will be presented. Important aspects of the discussion will include the form in which preference information is initially available, the impact of preference articulation techniques on the optimisation problems to be solved, the quality of the final solutions obtained, and user-related issues, such as visualisation and interaction. The talk will conclude with the identification of some opportunities for future work.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"83 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":"114919401","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: 4

A Novel Fully Evolved Kernel Method for Feature Computation from Multisensor Signal Using Evolutionary Algorithms 一种基于进化算法的多传感器信号特征计算全进化核方法

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.43

K. Iswandy, A. König

{"title":"A Novel Fully Evolved Kernel Method for Feature Computation from Multisensor Signal Using Evolutionary Algorithms","authors":"K. Iswandy, A. König","doi":"10.1109/HIS.2007.43","DOIUrl":"https://doi.org/10.1109/HIS.2007.43","url":null,"abstract":"The design of intelligent sensor systems requires sophisticated methods from conventional signal processing and computational intelligence. Currently, a significant part of the overall system architecture still has to be manually elaborated in a tedious and time consuming process by an experienced designer for each new application or modification. Clearly, an automatic method for auto-configuration of sensor systems would be salient. In this paper, we contribute to the optimization of the feature computation step in the overall system design, investigating Gaussian kernel methods. Our goal is to improve the kernel method of feature computation with consideration on including the adjustable magnitude parameter for Gaussian kernels or fully evolved Gaussian kernels, which are inspired by feature weighting concepts and are similar to RBF like neural network with correlation based kernel layer and linear combiner output layer. We compare this improved method with previous kernel methods using weighting method of multiobjective evolutionary optimization, i.e., genetic algorithms. In addition to the straightforward feature space from the optimized kernel layer, we complement the kernel layer by linear combiner layer, with weights computed by traditional IDA (linear discriminant analysis) in the loop of the optimization. In our experiments, we applied gas sensor benchmark data and the results showed that our novel method can achieve competitive or even better recognition accuracies and effectively reduce the computational complexity as well.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"82 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":"115604027","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: 2

Dataflow orchestration of image processing algorithms using high-level petri nets 使用高级petri网的图像处理算法的数据流编排

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.54

Björn Wagner, Andreas Dinges, P. Müller

引用次数: 3

Interpretable Granulation of Medical Data with DC 用DC对医学数据的可解释颗粒化

7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.15

Corrado Mencar, A. Consiglio, A. Fanelli

引用次数: 0