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IEEE Expert

Issue 5 • Date Oct. 1996

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Displaying Results 1 - 15 of 15
  • Oliver Selfridge - in from the start

    Publication Year: 1996 , Page(s): 15 - 17
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  • Data mining - here we go again

    Publication Year: 1996 , Page(s): 18 - 19
    Cited by:  Papers (1)
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  • Data mining and knowledge discovery: making sense out of data

    Publication Year: 1996 , Page(s): 20 - 25
    Cited by:  Papers (59)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2004 KB)  

    Current computing and storage technology is rapidly outstripping society's ability to make meaningful use of the torrent of available data. Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store. View full abstract»

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  • Scalable discovery of informative structural concepts using domain knowledge

    Publication Year: 1996 , Page(s): 59 - 68
    Cited by:  Papers (8)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3444 KB)  

    The subdue system evaluates the benefits of using domain knowledge to guide the discovery of repetitive, functional substructures in large structural databases. Results show that domain-specific knowledge improves the search for such substructures and enables greater data compression View full abstract»

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  • Constructing Bayesian networks to predict uncollectible telecommunications accounts

    Publication Year: 1996 , Page(s): 45 - 51
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1272 KB)  

    The complexities of building models that can predict whether a customer account or transaction is collectible are greater than most current learning systems can handle. The authors describe software that builds Bayesian network models for such predictions. They also examine how varying model parameters and hence model structure can affect predictive accuracy View full abstract»

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  • Visualization support for data mining

    Publication Year: 1996 , Page(s): 69 - 75
    Cited by:  Papers (6)  |  Patents (2)
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    WinViz uses parallel coordinates, a computational geometry method, to let users visually formulate queries and analyze results. When integrated with machine-learning algorithms, WinViz provides a powerful tool for data mining View full abstract»

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  • Reality check for data mining

    Publication Year: 1996 , Page(s): 26 - 33
    Cited by:  Papers (12)  |  Patents (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2360 KB)  

    Data-mining tools let business managers make profitable use of the massive quantities of information their companies collect. This article looks at two particularly useful data-mining applications View full abstract»

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  • It's time for your evaluation

    Publication Year: 1996 , Page(s): 10 - 11
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    If there is a word that strikes fear into the hearts of speech and natural-language researchers, it is “evaluation”. It's not that we don't like evaluation-mostly, we do-it's just that we've developed this knee-jerk response: “Another evaluation? Already? But we just did an evaluation!” Well, that's an exaggeration. But funder-promoted, common evaluations have profoundly affected our research in years past. In these evaluations, researchers agree on a number of tasks and criteria for success, then work independently on those tasks. In many cases, funding agencies provide sample inputs and outputs in advance, but withhold test data until “evaluation day”. Several months later, participating researchers gather at a specialized workshop to discuss results and techniques. Whether you're interested in machine translation, morphology, speech-query processing, information extraction, or information retrieval, there is a common evaluation somewhere where you can test your ideas in an empirical, quantitatively scored setting. The author looks at some strengths and weaknesses of these common evaluations View full abstract»

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  • Stock selection using rule induction

    Publication Year: 1996 , Page(s): 52 - 58
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2776 KB)  

    High-quality financial databases have existed for many years, but human analysts can only scratch the surface of the wealth of knowledge buried in this data. Using the rule-induction technology in the Recon data-mining system, an investment strategy based purely on the learned rules can generate significant profits View full abstract»

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  • The evolution of a decision support architecture for polymer composites design

    Publication Year: 1996 , Page(s): 77 - 83
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2636 KB)  

    Polymer composites-lightweight, strong, and energy-efficient materials-offer significant advantages to durable-goods manufacturers and to performance-driven markets such as the aerospace industry. In describing a knowledge-based architecture they've developed for automating the design of such materials, the authors trace the evolution of their approach to polymer composites design View full abstract»

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  • Content distribution on the Web

    Publication Year: 1996 , Page(s): 8 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (416 KB)  

    The efficient distribution of content-any information that can be communicated electronically-has a promising future on the Web. Content includes the articles and literature we are accustomed to reading in newspapers, magazines, and books; the music we listen to on the radio; and the programs and movies we watch on television and in theaters. Because the Web greatly improves on existing information channels, it has the potential to become the primary medium for all forms of content View full abstract»

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  • Getting what you deserve from data

    Publication Year: 1996 , Page(s): 12 - 14
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    This article's focus appears at first to be a narrow, prescriptive little corner of the methodological landscape. Data analysis is often dismissed as no more complicated than calculating some means and comparing them with t tests or the like. Consequently, experiments and analyses are inefficient, requiring more data than necessary to show an effect; they waste data, failing to show effects; and they sometimes induce hallucinations, suggesting effects that don't exist. Bad analysis can spoil an entire research program, so it warrants attention. I will discuss three common and easily fixed problems: accepting the null hypothesis, a misuse of statistical machinery; inadequate attention to sources of variance, leading to insignificant results and failure to notice interactions among factors; and multiple pairwise comparisons, leading to nonexistent effects View full abstract»

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  • Searching for the mother lode: tales of the first data miners

    Publication Year: 1996 , Page(s): 4 - 7
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    What do NEA coaches, foster children, and astronomers have in common? The answer: data mining. In the past year, KDD (knowledge discovery and data mining) has taken center stage in advanced technology groups around the world. The first generation of KDD applications are seeing the light of day. The author interviewed data miners from the foster care system in the state of Washington, assistant coaches in the US National Basketball Association, investigators with the US Department of the Treasury, consultants at NCR, and the principal astronomer of the largest sky survey ever undertaken View full abstract»

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  • Mining geophysical data for knowledge

    Publication Year: 1996 , Page(s): 34 - 44
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8172 KB)  

    Oasis is a flexible, extensible, and seamless environment for scientific data analysis, knowledge discovery, visualization, and collaboration. The authors describe how Oasis can help explore data analysis and data mining of spatio-temporal phenomena from large geophysical data sets View full abstract»

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  • Relevance and insight in experimental studies

    Publication Year: 1996 , Page(s): 11 - 12
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    As its name suggests, artificial intelligence is a science of the artificial. As with other conscious creations, there is a great temptation to assume that we can understand the behavior of AI systems entirely through formal analysis. However, the complexity of most AI constructs makes this impractical, forcing us to rely on the same experimental approach that has been so useful in the natural sciences. Many of the same issues and methods apply directly to AI systems, including the need to identify clearly one's dependent and independent variables, the importance of careful experimental design, and the need to average across random variables outside one's control. However, beyond these obvious features, a compelling experimental study of intelligent behavior must satisfy two additional criteria: it must have relevance and it must produce insight. I will illustrate these ideas with examples from machine learning, one of the most experimentally oriented subfields within artificial intelligence. Moreover, because AI researchers are often concerned with extending some existing method to improve its behavior, I will focus on this paradigm View full abstract»

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Aims & Scope

This Periodical ceased production in 1997. The current retitled publication is IEEE Intelligent Systems.

Full Aims & Scope