English  |  正體中文  |  简体中文  |  Items with full text/Total items : 93288/109022 (86%)
Visitors : 20964608      Online Users : 262
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/64781


    Title: Advances in Machine Learning Applications in Software Engineering
    Authors: Zhang, Du;Zhang, Du;蔡進發;Jeffrey, J.P.Tsai
    Contributors: 生物與醫學資訊學系
    Date: 2007
    Issue Date: 2013-12-02 17:22:43 (UTC+8)
    Publisher: IGI Publishing Inc., PA
    Abstract: Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering also offers readers direction for future work in this emerging research field.
    Appears in Collections:[生物資訊與醫學工程學系 ] 專書

    Files in This Item:

    There are no files associated with this item.



    All items in ASIAIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback