17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)最新文献_第5页

Fuzzy contributive games: an extension to the game of civic duty 模糊贡献博弈:公民义务博弈的延伸

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.67

Amir Danak, A. Rahimi-Kian

引用次数: 0

Spatio-temporal relevant logic as the logical basis for spatio-temporal information systems 时空相关逻辑是时空信息系统的逻辑基础

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.115

Jingde Cheng

引用次数: 4

Multi-robot avoidance based on co-evolution computation 基于协同进化计算的多机器人回避

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.88

Huaqing Min, XiJing Zheng, Yansheng Lu

引用次数: 1

Learning lazy naive Bayesian classifiers for ranking 学习懒惰朴素贝叶斯分类器排序

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.80

Liangxiao Jiang, Yuanyuan Guo

{"title":"Learning lazy naive Bayesian classifiers for ranking","authors":"Liangxiao Jiang, Yuanyuan Guo","doi":"10.1109/ICTAI.2005.80","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.80","url":null,"abstract":"Naive Bayes (simply NB) has been well-known as an effective and efficient classification algorithm. However, it is based on the conditional independence assumption that it is often violated in applications. In addition, in many real-world data mining applications, however, an accurate ranking of instances is often required rather than an accurate classification. For example, a ranking of customers in terms of the likelihood that they buy one's products is useful in direct marketing. In this paper, we firstly investigate the ranking performance of some lazy learning algorithms for extending naive Bayes. The ranking performance is measured by Hand and Till (2001) and Bradley (1997). We observe that they can not significantly improve naive Bayes' ranking performance. Motivated by this fact and aiming at improving naive Bayes with accurate ranking, we present a new lazy learning algorithm, called lazy naive Bayes (simply LNB), to extend naive Bayes for ranking. We experimentally tested our algorithm, using the whole 36 UCI data sets (Blake and Merz, 2000) recommended by Weka, and compared it to NB and C4.4 (Provost and Domingos, 2003) measured by AUC. The experimental results show that our algorithm significantly outperforms both NB and C4.4","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133733049","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: 23

Minimality and convexity properties in spatial CSPs 空间csp的极小性和凸性

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.85

Priti Chandra, A. K. Pujari

{"title":"Minimality and convexity properties in spatial CSPs","authors":"Priti Chandra, A. K. Pujari","doi":"10.1109/ICTAI.2005.85","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.85","url":null,"abstract":"The research in qualitative reasoning and in spatial CSP is always investigated in the backdrop of its temporal counterpart - qualitative temporal reasoning and TCSP. Unlike the case of interval algebra (IA), the composition table of RCC, IA's so-called spatial counterpart, is in general neither complete nor extensional, the compositional consistency can be still a valid reasoning mechanism. Even in such a restricted situation, many of the known properties of IA have not been investigated for validity in the context of RCC. We address, in this paper two such properties-convexity and minimality. The importance of minimality cannot be underestimated as in a minimal network every label is feasible and hence determining all the consistent scenarios can be accomplished very efficiently. It is known that path consistency does not yield a minimal network for tractable classes of RCC-8. We represent RCC-8 relations as a partially ordered set and exploit the properties of partial ordering to derive very interesting theoretical results. We show here that there exists a convex class of relations of RCC-8 for which path consistency yields a minimal network. Our results are very important as it gives a sufficient condition for minimality and useful to generate all consistent scenarios whenever compositional consistency is a valid reasoning mechanism","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696410","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

Cellular ants: combining ant-based clustering with cellular automata 元胞蚂蚁:结合基于蚁的聚类与元胞自动机

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.47

A. V. Moere, Justin James Clayden

引用次数: 15

Training RBF networks using a DE algorithm with adaptive control 使用带有自适应控制的DE算法训练RBF网络

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.123

Junhong Liu, J. Mattila, J. Lampinen

引用次数: 9

A semi-supervised learning method for remote sensing data mining 一种用于遥感数据挖掘的半监督学习方法

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.17

Ranga Raju Vatsavai, S. Shekhar, T. Burk

{"title":"A semi-supervised learning method for remote sensing data mining","authors":"Ranga Raju Vatsavai, S. Shekhar, T. Burk","doi":"10.1109/ICTAI.2005.17","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.17","url":null,"abstract":"New approaches are needed to extract useful patterns from increasingly large multi-spectral remote sensing image databases in order to understand global climatic changes, vegetation dynamics, ocean processes, etc. Supervised learning, which is often used in land cover (thematic) classification of remote sensing imagery, requires large amounts of accurate training data. However, in many situations it is very difficult to collect labels for all training samples. In this paper we explore methods that utilize unlabeled samples in supervised learning for thematic information extraction from remote sensing imagery. Our objectives are to understand the impact of parameter estimation with small learning samples on classification accuracy, and to augment the parameter estimation with unlabeled training samples to improve land cover predictions. We have developed a semi-supervised learning method based on the expectation-maximization (EM) algorithm, and maximum likelihood and maximum a posteriori classifiers. This scheme utilizes a small set of labeled and a large number of unlabeled training samples. We have conducted several experiments on multi-spectral images to understand the impact of unlabeled samples on the classification performance. Our study shows that though in general classification accuracy improves with the addition of unlabeled training samples, it is not guaranteed to get consistently higher accuracies unless sufficient care is exercised when designing a semi-supervised classifier","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133851287","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: 29

Efficient pattern discovery for semistructured data 半结构化数据的有效模式发现

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.63

Zhou Feng, W. Hsu, M. Lee

引用次数: 10

An ant algorithm for single-hop wavelength assignment in WDM mesh network WDM网状网络中单跳波长分配的蚁群算法

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.33

T. S. Chin

引用次数: 10