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2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)最新文献

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Fast Modeling of Curved Object from Two Images 基于两幅图像的曲面物体快速建模
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.27
Jiguo Zeng, Yan Zhang, Chen Liu, Shouyi Zhan
{"title":"Fast Modeling of Curved Object from Two Images","authors":"Jiguo Zeng, Yan Zhang, Chen Liu, Shouyi Zhan","doi":"10.1109/HIS.2006.27","DOIUrl":"https://doi.org/10.1109/HIS.2006.27","url":null,"abstract":"A method of modeling curved object from two images is proposed. 3D curved object is represented by the standard parametric surface. The silhouettes of the images photographed from the frontal and lateral position can be obtained by user interaction. They are inputted as the linear constraints of the quadratic object function to find the smoothest curved surfaces of the object. In order to reduce the amount of user interaction, Gaussian mixture model based image segmentation algorithm is used to obtain the silhouettes. We demonstrate the effectiveness of this method in modeling real curved objects.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125848923","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
A Novel Microarray Gene Selection Method Based on Consistency 一种基于一致性的微阵列基因选择方法
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.7
Yingjie Hu, Shaoning Pang, I. Havukkala
{"title":"A Novel Microarray Gene Selection Method Based on Consistency","authors":"Yingjie Hu, Shaoning Pang, I. Havukkala","doi":"10.1109/HIS.2006.7","DOIUrl":"https://doi.org/10.1109/HIS.2006.7","url":null,"abstract":"Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, we address this issue as a consistency problem. We propose a new concept of performance-based consistency and a new novel gene selection method, Genetic Algorithm Gene Selection method in terms of consistency (GAGSc). The proposed consistency concept and GAGSc method were investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy. More importantly, GAGSc has demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115542042","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
A Contribution to Automatic Design of Image Processing Systems--Breeding Optimized Non-Linear and Oriented Kernels for Texture Analysis 对图像处理系统自动设计的贡献——培育用于纹理分析的优化非线性和定向核
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.2
Stefanie Peters, A. König
{"title":"A Contribution to Automatic Design of Image Processing Systems--Breeding Optimized Non-Linear and Oriented Kernels for Texture Analysis","authors":"Stefanie Peters, A. König","doi":"10.1109/HIS.2006.2","DOIUrl":"https://doi.org/10.1109/HIS.2006.2","url":null,"abstract":"The rapid development in image processing technology allows the tackling of application of increasing complexity. For efficient design of application specific systems design automation techniques are required. This paper reports on activities for texture classification employing non-linear oriented kernels configured by evolutionary optimization techniques. Our approach was tested with benchmark and application data from leather inspection and found viable and competitive in both cases.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"458 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131073494","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
Genetic Based Machine Learning: Merging Pittsburgh and Michigan, an Implicit Feature Selection Mechanism and a New Crossover Operator 基于遗传的机器学习:合并匹兹堡和密歇根，一个隐式特征选择机制和一个新的交叉算子
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.28
C. Pitangui, Gerson Zaverucha
{"title":"Genetic Based Machine Learning: Merging Pittsburgh and Michigan, an Implicit Feature Selection Mechanism and a New Crossover Operator","authors":"C. Pitangui, Gerson Zaverucha","doi":"10.1109/HIS.2006.28","DOIUrl":"https://doi.org/10.1109/HIS.2006.28","url":null,"abstract":"This paper presents, for discrete data, a new crossover operator to be used together with the Natural Coding. This new operator, differently of the already existing one, beyond possessing high speed of application, explores the search space in the same way that the crossover operator used when the binary representation is adopted. Additionally, this work presents a new way of representing the mechanism of Feature Selection. This representation provides a high economy of memory, fact that supplies to the system a double genetic exploration. The system uses a hybridization of Pittsburgh and Michigan approaches. We compare our system with the C4.5 algorithm in some datasets from UCI. Results show that the proposed system is very robust and can achieve high accuracy with simple rules.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124349783","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: 9
A Hybrid Rough Set--Particle Swarm Algorithm for Image Pixel Classification 图像像素分类的混合粗糙集-粒子群算法
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.5
Swagatam Das, A. Abraham, S. Sarkar
{"title":"A Hybrid Rough Set--Particle Swarm Algorithm for Image Pixel Classification","authors":"Swagatam Das, A. Abraham, S. Sarkar","doi":"10.1109/HIS.2006.5","DOIUrl":"https://doi.org/10.1109/HIS.2006.5","url":null,"abstract":"This article presents a framework to hybridize the rough set theory with a famous swarm intelligence algorithm known as Particle Swarm Optimization (PSO). The hybrid rough-PSO technique has been used for grouping the pixels of an image in its intensity space. Medical and remote sensing satellite images become corrupted with noise very often. Fast and efficient segmentation of such noisy images (which is essential for their further interpretation in many cases) has remained a challenging problem for years. In this work, we treat image segmentation as a clustering problem. Each cluster is modeled with a rough set. PSO is employed to tune the threshold and relative importance of upper and lower approximations of the rough sets. Davies-Bouldin clustering validity index is used as the fitness function, which is minimized while arriving at an optimal partitioning.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122249666","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: 24
Is That Possible? (Or is it Probable?) 这可能吗?(或者这是可能的?)
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.38
Tom Gedeon
{"title":"Is That Possible? (Or is it Probable?)","authors":"Tom Gedeon","doi":"10.1109/HIS.2006.38","DOIUrl":"https://doi.org/10.1109/HIS.2006.38","url":null,"abstract":"In settings where we have significant amounts of data, the probability of events occurring can be calculated. But life is not like that. Human beings must cope with situations where we encounter only a small number of events, handle ambiguous and imprecise information, and respond correctly. Also, arguably, possibility values are more relevant to human beings in the understanding of risk and danger than probability values of unlikely events. In this work I describe some work in transforming low occurrence counts into estimation of imprecise probability.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"577 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128386319","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
Vise: A System for Visualizing Salient Events in a Text Stream 可视化文本流中突出事件的系统
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.76
G. Fung
{"title":"Vise: A System for Visualizing Salient Events in a Text Stream","authors":"G. Fung","doi":"10.1109/HIS.2006.76","DOIUrl":"https://doi.org/10.1109/HIS.2006.76","url":null,"abstract":"In this paper, we present a system called Vise for visualizing salient events in a text stream according to the users' interests. A text stream is a sequence of chronological ordered documents. News articles, email and newsgroup postings are some typical examples of text stream. Through Vise, a user can visualize the events resides in a text stream by providing a set of keywords that are related to the events. A graph will be displayed to denote for the underlying patterns of the events. Yet, retrieving events in a text stream is a very difficult task due to the sparsity and noisiness of the features (keywords) in there. We solve these problems with the help of binomial distribution and some statis- tical theories. We have archived a stream of two-year news articles to evaluate the usability and the effec- tiveness of Vise. According to a subjective evaluation, the patterns of the events identified are justifiable and match our expectation. These favorable results indi- cated that our proposed system is highly effective and practical.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128582782","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
An Adaptive System for Improved Identification and Removal of Noise from Single Trial EEG/MEG via Model Order Estimation in ICA 一种基于ICA模型阶数估计的改进单试验脑磁图噪声识别与去除的自适应系统
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.11
Carl Leichter
{"title":"An Adaptive System for Improved Identification and Removal of Noise from Single Trial EEG/MEG via Model Order Estimation in ICA","authors":"Carl Leichter","doi":"10.1109/HIS.2006.11","DOIUrl":"https://doi.org/10.1109/HIS.2006.11","url":null,"abstract":"An adaptive model order estimation method for Independent Component Analysis (ICA) in EEG/MEG data is presented. This technique seeks to extract the minimum number of components necessary for effective Blind Source Separation (BSS). Experimental results using synthesized noisy MEG data demonstrate the utility of this technique. Model order estimation is used in the extraction of baseline noise components which will serve as templates for subsequent identification and removal of noise. These templates are used to remove noise from a data set containing a somatosensory evoked response (SSR) potential; model order estimation was also used to decompose the SSR data set.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129537492","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
A Task Decomposition Algorithm Using Mixtures of Normal Distributions for Classification Problems 基于混合正态分布的分类问题任务分解算法
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.9
S. Ishihara, H. Igarashi
{"title":"A Task Decomposition Algorithm Using Mixtures of Normal Distributions for Classification Problems","authors":"S. Ishihara, H. Igarashi","doi":"10.1109/HIS.2006.9","DOIUrl":"https://doi.org/10.1109/HIS.2006.9","url":null,"abstract":"This paper proposes an algorithm for decomposing a multi-class classification problem into a set of two-class classification problems. The algorithm divides a set of input pattern vectors in each class into subsets according to the distribution of the selected input pattern vectors. The distribution is represented by a mixture of normal distributions, and the number of subsets is defined by using MDL criterion. The algorithm can be applied for constructing an effective modular neural network. We show also the experimental results of the construction and the advantages of the algorithm.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129949701","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
VOGA: Variable Ordering Genetic Algorithm for Learning Bayesian Classifiers
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.77
E. B. D. Santos, Estevam Hruschka
{"title":"VOGA: Variable Ordering Genetic Algorithm for Learning Bayesian Classifiers","authors":"E. B. D. Santos, Estevam Hruschka","doi":"10.1109/HIS.2006.77","DOIUrl":"https://doi.org/10.1109/HIS.2006.77","url":null,"abstract":"This work proposes a hybrid approach to help the process of learning a Bayesian Classifier (BC) from data. The proposed method named VOGA (and its variant VOGA+) uses a Genetic Algorithm to optimize the BC learning process by means of the identification of an adequate variables ordering. The main contribution of VOGA and VOGA+ is the use information about the class variable when defining the most suitable variable ordering. Trying to optimize the GA initial population, VOGA+ ranks the attributes based on the class variable. Experiments performed in a number of datasets revealed that both methods are promising and VOGA+ tends to be favored domains having higher number of variables.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130885084","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: 7
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