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A Tax That Could Fix Big Tech
Paul Romer
You Are Not as Good at Kissing as You Think. But You Are Better at Dancing.
Spencer Greenberg
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The Joy of Standards
Andrew Russell
Lee Vinsel
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At Work, Everyone Is in Fact Talking About You All the Time
Megan Greenwell
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기계는 어떻게 생각하고 학습하는가
뉴 사이언티스트 외
Chef's Table
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The Brontës' Secret
Judith Shulevitz
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Keyphrase extraction is a task with many applications in information retrieval, text mining, and natural language processing. In this paper, a keyphrase extraction approach based on neural network is proposed. To determine whether a phrase is a keyphrase, the following features of a phrase in a given document are adopted: its term frequency and inverted document frequency, whether to appear in the title or headings (subheadings) of the given document, and its frequency appearing in the paragraphs of the given document. The algorithm is evaluated by the standard information retrieval metrics of precision and recall, and human assessment.
Automatic Keyphrases Extraction from Document Using Neural Network | SpringerLink
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General Magic the Movie
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Jane Eyre and the Invention of Self
Karen Swallow Prior
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Author(s):Abstract:Managing large amounts of information and efficiently using this information in improved decision making has become increasingly challenging as businesses collect terabytes of data. Intelligent solutions, based on neural networks (NNs) and genetic algorithms (GAs), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. The current study provides an overview for the operations researcher of the neural networks and genetic algorithms methodology, as well as their historical and current use in business. The main aim is to present and focus on the wide range of business areas of NN and GA applications, avoiding an in‐depth analysis of all the applications – with varying success – recorded in the literature. This review reveals that, although still regarded as a novel methodology, NN and GA are shown to have matured to the point of offering real practical benefits in many of their applications.Keywords: Type: Literature reviewPublisher: Emerald Group Publishing LimitedCopyright:Citation: Kostas Metaxiotis, John Psarras, (2004) "The contribution of neural networks and genetic algorithms to business decision support: Academic myth or practical solution?", Management Decision, Vol. 42 Issue: 2, pp.229-242, https://doi.org/10.1108/00251740410518534Downloads: The fulltext of this document has been downloaded 1671 times since 2012
The contribution of neural networks and genetic algorithms to business decision support
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July 2023
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