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

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Reachability analysis for uncertain SSPs 不确定ssp的可达性分析
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1142/S0218213007003527
O. Buffet
{"title":"Reachability analysis for uncertain SSPs","authors":"O. Buffet","doi":"10.1142/S0218213007003527","DOIUrl":"https://doi.org/10.1142/S0218213007003527","url":null,"abstract":"Stochastic shortest path problems (SSPs) can be efficiently dealt with by the real-time dynamic programming algorithm (RTDP). Yet, RTDP requires that a goal state is always reachable. This paper presents an algorithm checking for goal reachability, especially in the complex case of an uncertain SSP where only a possible interval is known for each transition probability. This gives an analysis method for determining if SSP algorithms such as RTDP are applicable, even if the exact model is not known. We aim at a symbolic analysis in order to avoid a complete state-space enumeration","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"30 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":"124998113","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
Query size estimation using clustering techniques 使用聚类技术估计查询大小
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.105
Xiaoyuan Su, M. Kubát, M. Tapia, C. Hu
{"title":"Query size estimation using clustering techniques","authors":"Xiaoyuan Su, M. Kubát, M. Tapia, C. Hu","doi":"10.1109/ICTAI.2005.105","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.105","url":null,"abstract":"For managing the performance of database management systems, we need to be able to estimate the size of queries. Query size estimation (QSE) is difficult if the queries are associated with more than one attribute. Here, we propose, and experimentally evaluate, a novel technique that builds on cluster analysis. Empirical results indicate that, in particular, density-based clustering QSE techniques are beneficial for medium and large sized databases where they compare favourably with partitioning clustering QSE ones such as k-means. This is observed especially in the case of noisy and dense datasets","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"27 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":"126747481","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
A fuzzy approach of decision making for an airline 航空公司决策的模糊方法
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.8
S. Charfeddine, F. Mora-Camino, Karim Zbidi
{"title":"A fuzzy approach of decision making for an airline","authors":"S. Charfeddine, F. Mora-Camino, Karim Zbidi","doi":"10.1109/ICTAI.2005.8","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.8","url":null,"abstract":"In the air transport sector where the uncertainty is very frequent due to the high sensitivity of the activity to many factors that influence the behavior of the consumers. Fuzzy logic can be used to produce estimates which take into account the vagueness of the operating environment. In this sector, many important decisions about the supply of service are based on these estimates. In this paper an analysis of the optimal supply conditions under fuzzy demand information is considered for a market where only one carrier is operating. The carrier's supply decision making process is investigated, first when the demand function is obtained through classical methods then when it is given by a fuzzy function. The main advantages of the new approach are then discussed","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"18 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":"126782370","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: 3
Modular general fuzzy hypersphere neural network 模块化通用模糊超球神经网络
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.86
P. Patil, S. Kulkarni, A. Patil, D. Doye, U. Kulkarni
{"title":"Modular general fuzzy hypersphere neural network","authors":"P. Patil, S. Kulkarni, A. Patil, D. Doye, U. Kulkarni","doi":"10.1109/ICTAI.2005.86","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.86","url":null,"abstract":"This paper describes modular general fuzzy hypersphere neural network (MGFHSNN) with its learning algorithm, which is an extension of general fuzzy hypersphere neural network (GFHSNN) proposed by Kulkarni, Doye and Sontakke (2002) that combines supervised and unsupervised learning in a single algorithm so that it can be used for pure classification, pure clustering and hybrid classification/clustering. MGFHSNN offers higher degree of parallelism since each module is exposed to the patterns of only one class and trained without overlap test and removal, unlike in fuzzy hypersphere neural network (FHSNN) by U.V. Kulkarni et al. (2001), leading to reduction in training time. In proposed algorithm each module captures peculiarity of only one particular class and found superior in terms of generalization and training time with equivalent testing time. Thus, it can be used for voluminous realistic database, where new patterns can be added on fly","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"38 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":"126933800","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: 6
Intrusion detection based on cross-correlation of system call sequences 基于系统调用序列互相关的入侵检测
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.78
Xiaoqiang Zhang, Zhongliang Zhu, P. Fan
{"title":"Intrusion detection based on cross-correlation of system call sequences","authors":"Xiaoqiang Zhang, Zhongliang Zhu, P. Fan","doi":"10.1109/ICTAI.2005.78","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.78","url":null,"abstract":"A new light-weight approach, based on the cross-correlation of system call sequences, is presented to identify normal or intrusive program behavior. The program behavior is represented by the cross-correlation value which can be used to indicate the similarity between two sequences. If two sequences are same, the cross-correlation between them will achieve the maximum value. This method of characterizing program behavior by using cross-correlation offers significant computational advantages over HMM (hidden Markov model) or NN (neural network) methods due to the absence of unnecessary training process. Our experiments using UNM (University of New Mexico) audit data show that the cross-correlation based method can effectively detect intrusive attacks and achieve a low false positive rate","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"129 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":"114411385","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
Reasoning technique for extended fuzzy description logics 扩展模糊描述逻辑的推理技术
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.107
Yanhui Li, Baowen Xu, Jianjiang Lu, Dazhou Kang, Jie Xu
{"title":"Reasoning technique for extended fuzzy description logics","authors":"Yanhui Li, Baowen Xu, Jianjiang Lu, Dazhou Kang, Jie Xu","doi":"10.1109/ICTAI.2005.107","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.107","url":null,"abstract":"A family of extended fuzzy description logics, which includes a framework of syntax, semantics, knowledge base form and reasoning tasks, is proposed to enable representation and reasoning for complex fuzzy information. This paper discusses the reasoning technique for reasoning tasks of extended fuzzy description logics, which adopts classical description logics to discretely simulate extended fuzzy description logic in polynomial time and reuses the existing reasoning result to prove the complexity of reasoning tasks of extended fuzzy description logics","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"28 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":"114599050","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: 3
ACE: an aggressive classifier ensemble with error detection, correction and cleansing ACE:具有错误检测、纠正和清理功能的主动分类器集成
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.23
Yan Zhang, Xingquan Zhu, Xindong Wu, Jeffrey P. Bond
{"title":"ACE: an aggressive classifier ensemble with error detection, correction and cleansing","authors":"Yan Zhang, Xingquan Zhu, Xindong Wu, Jeffrey P. Bond","doi":"10.1109/ICTAI.2005.23","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.23","url":null,"abstract":"Learning from noisy data is a challenging and reality issue for real-world data mining applications. Common practices include data cleansing, error detection and classifier ensembling. The essential goal is to reduce noise impacts and enhance the learners built from the noise corrupted data, so as to benefit further data mining procedures. In this paper, we present a novel framework that unifies error detection, correction and data cleansing to build an aggressive classifier ensemble for effective learning from noisy data. Being aggressive, the classifier ensemble is built from the data that has been preprocessed by the data cleansing and correcting techniques. Experimental comparisons will demonstrate that such an aggressive classifier ensemble is superior to the model built from the original noisy data, and is more reliable in enhancing the learning theory extracted from noisy data sources, in comparison with simple data correction or cleansing efforts","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"14 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":"121997297","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: 10
Element matching by concatenating linguistic-based matchers and constraint-based matcher 通过连接基于语言的匹配器和基于约束的匹配器来进行元素匹配
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.64
Jingtao Zhou, Shusheng Zhang, Mingwei Wang, Han Zhao, Chao Zhang, Peng Li, Xiaofeng Dong, Kefei Wang
{"title":"Element matching by concatenating linguistic-based matchers and constraint-based matcher","authors":"Jingtao Zhou, Shusheng Zhang, Mingwei Wang, Han Zhao, Chao Zhang, Peng Li, Xiaofeng Dong, Kefei Wang","doi":"10.1109/ICTAI.2005.64","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.64","url":null,"abstract":"Although a lot of previous work on schema matching has developed many partial automatic matches for specific application domains, combining multiple match techniques enables achieving high accuracy for a large variety of match circumstances. In this context, we present a schema-based element matching approach that concatenates linguistic-based matchers and a constraint-based matcher. We propose a basic processing of our element level match approach in terms of a sequence of linguistic-based match and constraint-based match. We also provide a composite element name matcher to automatically combine linguistic-based match algorithms with a maximum priority strategy, and a neural network matcher to categorize elements of schemas by using element constraints with results from composite name matcher for joint consideration of multiple criteria. The concatenation of composite name matcher and neural network matcher enable our approach to adapt to more complex matching circumstance","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"30 5 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":"131389247","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
Automatic segmentation of lung areas based on SNAKES and extraction of abnormal areas 基于snake的肺区域自动分割及异常区域提取
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.44
Y. Itai, Hyoungseop Kim, S. Ishikawa, S. Katsuragawa, T. Ishida, Katsumi Nakamura, A. Yamamoto
{"title":"Automatic segmentation of lung areas based on SNAKES and extraction of abnormal areas","authors":"Y. Itai, Hyoungseop Kim, S. Ishikawa, S. Katsuragawa, T. Ishida, Katsumi Nakamura, A. Yamamoto","doi":"10.1109/ICTAI.2005.44","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.44","url":null,"abstract":"Segmentation for lung areas from CT images is an important task on understanding tissue construction, computing and extracting abnormal areas. Many segmentation methods based on contour model are presented. SNAKES (active contour model), on the other hand, are used extensively in computer vision and image processing applications particularly to locate the object boundaries. In lung segmentation, SNAKES is used for extracting the detail of ROI. However, a completely automatic segmentation method is not yet published, since it needs some manual efforts for initial contouring and constructing the contour models. In this paper, we propose a segmentation method for lung areas based on SNAKES without considering any manual operations. Furthermore, abnormal area including ground-glass opacity or lung cancer is classified by voxel density on the CT slice set. Experiment is performed employing nine thorax CT image sets and satisfactory results are obtained. Obtained results are shown along with a discussion","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"4 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":"131850376","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: 46
Subgoal ordering and granularity control for incremental planning 增量计划的子目标排序和粒度控制
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1142/S0218213007003515
Chih-Wei Hsu, Yixin Chen
{"title":"Subgoal ordering and granularity control for incremental planning","authors":"Chih-Wei Hsu, Yixin Chen","doi":"10.1142/S0218213007003515","DOIUrl":"https://doi.org/10.1142/S0218213007003515","url":null,"abstract":"In this paper, we study strategies in incremental planning for ordering and grouping subproblems partitioned by the subgoals of a planning problem when each sub-problem is solved by a basic planner. To generate a rich set of partial orders for ordering subproblems, we propose a new ordering algorithm based on a relaxed plan built from the initial state to the goal state. The new algorithm considers both the initial and the goal states and can effectively order subgoals in such a way that greatly reduces the number of invalidations during incremental planning. We have also considered trade-offs between the granularity of the subgoal sets and the complexity of solving the overall planning problem. We show an optimal region of grain size that minimizes the total complexity of incremental planning. We propose an efficient strategy to dynamically adjust the grain size in partitioning in order to operate in this optimal region. We further evaluate a redundant-execution scheme that uses two different subgoal orders in order to improve the quality of the plans generated without greatly sacrificing run-time efficiency. Experimental results on using three basic planners (metric-FF, YAHSP, and LPG-TD-speed) show that our strategies are general for improving the time and quality of each of these planners across various benchmarks","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"1 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":"130893500","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: 8
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