IEEE Intelligent Systems

Issue 2 • Mar-Apr 2004

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Displaying Results 1 - 21 of 21
  • Cleaning the spurious links in data

    Publication Year: 2004, Page(s):28 - 33
    Cited by:  Papers (11)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (269 KB) | HTML iconHTML

    Data quality problems can arise from abbreviations, data entry mistakes, duplicate records, missing fields, and many other sources. These problems proliferate when you integrate multiple data sources in data warehousing, federated databases, and global information systems. A newly discovered class of erroneous data is spurious links, where a real-world entity has multiple links that might not be p... View full abstract»

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  • IEEE Computer Society - Information

    Publication Year: 2004, Page(s): 81
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  • Analyzing software measurement data with clustering techniques

    Publication Year: 2004, Page(s):20 - 27
    Cited by:  Papers (66)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (435 KB) | HTML iconHTML

    For software quality estimation, software development practitioners typically construct quality-classification or fault prediction models using software metrics and fault data from a previous system release or a similar software project. Engineers then use these models to predict the fault proneness of software modules in development. Software quality estimation using supervised-learning approache... View full abstract»

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  • The Janus principle

    Publication Year: 2004, Page(s):78 - 80
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (269 KB) | HTML iconHTML

    We find here an allegory to the apprentice-expert continuum in the acquisition of knowledge, and so we name a principle of human-centered computing after Janus. This principle deals with the distinction between performance and training, and its implications for intelligent technologies. View full abstract»

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  • Scoring levels of categorical variables with heterogeneous data

    Publication Year: 2004, Page(s):14 - 19
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (281 KB) | HTML iconHTML

    Heterogeneous (mixed-type) data present significant challenges in both supervised and unsupervised learning. The situation is even more complicated when nominal variables have several levels (values) that make using indicator variables (for every categorical level) infeasible. With unsupervised learning, several fairly involved, computationally intensive, nonlinear multivariate techniques iterativ... View full abstract»

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  • Hybrid neural document clustering using guided self-organization and WordNet

    Publication Year: 2004, Page(s):68 - 77
    Cited by:  Papers (16)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (574 KB) | HTML iconHTML

    Document clustering is text processing that groups documents with similar concepts. It's usually considered an unsupervised learning approach because there's no teacher to guide the training process, and topical information is often assumed to be unavailable. A guided approach to document clustering that integrates linguistic top-down knowledge from WordNet into text vector representations based o... View full abstract»

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  • The JSAI and AI activity in Japan

    Publication Year: 2004, Page(s):66 - 67
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (193 KB) | HTML iconHTML

    Japan has a long history of AI research, in fact, research on computer vision and natural language processing was already in progress in the early seventies. However, two events that started Japan's wave of AI research were the International Joint Conference on Artificial Intelligence (IJCAI) in Tokyo in 1979 and the Fifth Generation Computer System Project which the Ministry of International Trad... View full abstract»

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  • Parameter tuning for induction-algorithm-oriented feature elimination

    Publication Year: 2004, Page(s):40 - 49
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (309 KB) | HTML iconHTML

    Feature selection has long been an active research topic in machine learning. Beginning with an empty set of features, it selects features most necessary for learning a target concept. Feature elimination, a newer technique, starts out with a full set of features and eliminates those most unnecessary for learning the target concept. Feature elimination tends to be more effective, can capture inter... View full abstract»

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  • Toward intelligent driver-assistance and safety warning system

    Publication Year: 2004, Page(s):8 - 11
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (558 KB) | HTML iconHTML

    A major problem associated with the rapid growth in automotive production is an increase in traffic congestion and accidents, especially in big cities of China. To solve the problem, the government has been increasing funds for improving the traffic infrastructure, enforcing traffic laws, and educating drivers about traffic regulations. In addition, research institutes have launched R&D projects i... View full abstract»

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  • Advanced data preprocessing for intersites Web usage mining

    Publication Year: 2004, Page(s):59 - 65
    Cited by:  Papers (47)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (301 KB) | HTML iconHTML

    Web usage mining applies data mining procedures to analyze user access of Web sites. As with any KDD (knowledge discovery and data mining) process, WUM contains three main steps: preprocessing, knowledge extraction, and results analysis. We focus on data preprocessing, a fastidious, complex process. Analysts aim to determine the exact list of users who accessed the Web site and to reconstitute use... View full abstract»

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  • Polishing blemishes: issues in data correction

    Publication Year: 2004, Page(s):34 - 39
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (254 KB) | HTML iconHTML

    Data quality is crucial to any data analysis task. Many imperfection-handling techniques avoid overfitting or simply remove offending portions of the data. Polishing identifies blemishes in the data and makes corrections to retain and recover as much information as possible. When using information collected from channels susceptible to disturbances, data quality is a concern-especially when the pr... View full abstract»

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  • When virtual roberts race, science wins

    Publication Year: 2004, Page(s):4 - 7
    Cited by:  Patents (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (857 KB) | HTML iconHTML

    Sodarace, an asynchronous, interactive Web game that pits human-created virtual robots against artificial intelligence-based, machine-created virtual robots, has a clear winner: science. At www.sodarace.net, the game invites players to use a Java-based simulator called Sodaconstructor to create both terrains and virtual robots for racing. The site also contains three main forums: the Sodarace leag... View full abstract»

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  • Predicting customer behavior in telecommunications

    Publication Year: 2004, Page(s):50 - 58
    Cited by:  Papers (23)  |  Patents (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (268 KB) | HTML iconHTML

    The wireless service subscriber calls a customer service representative to complain about dropped calls. During the conversation with the customer, the CSR views a display that shows this customer's probability of churn-switching from this service provider to another-as well as the most probable reasons to churn and the best strategy to retain this customer. The CSR then quickly responds to the su... View full abstract»

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  • IEEE Intelligent Systems

    Publication Year: 2004, Page(s): 0_1
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2004, Page(s):0_2 - 1
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  • From the editor in chief - What if?

    Publication Year: 2004, Page(s):2 - 3
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  • Guest editors' introduction - Information enhancement for data mining

    Publication Year: 2004, Page(s):12 - 13
    Cited by:  Papers (12)
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  • Calendar

    Publication Year: 2004, Page(s):82 - 87
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  • Call for papers

    Publication Year: 2004, Page(s):87 - 88
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  • Editorial calendar

    Publication Year: 2004, Page(s): 89
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  • Call for papers

    Publication Year: 2004, Page(s): 90
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Aims & Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications.

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Meet Our Editors

Editor-in-Chief
Daniel Zeng
University of Arizona