IDEAS home Printed from https://ideas.repec.org/a/spr/envsyd/v39y2019i3d10.1007_s10669-019-09738-y.html
   My bibliography  Save this article

Advances in machine learning and decision making

Author

Listed:
  • Zachary A. Collier

    (Collier Research Systems)

  • James H. Lambert

    (University of Virginia)

  • Igor Linkov

    (US Army Engineer Research & Development Center)

Abstract

No abstract is available for this item.

Suggested Citation

  • Zachary A. Collier & James H. Lambert & Igor Linkov, 2019. "Advances in machine learning and decision making," Environment Systems and Decisions, Springer, vol. 39(3), pages 247-248, September.
    • Handle: RePEc:spr:envsyd:v:39:y:2019:i:3:d:10.1007_s10669-019-09738-y
    DOI: 10.1007/s10669-019-09738-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10669-019-09738-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10669-019-09738-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stephen Adams & Steven Greenspan & Maria Velez-Rojas & Serge Mankovski & Peter A. Beling, 2019. "Data-driven simulation for energy consumption estimation in a smart home," Environment Systems and Decisions, Springer, vol. 39(3), pages 281-294, September.
    2. Arun Varghese & Tao Hong & Chelsea Hunter & George Agyeman-Badu & Michelle Cawley, 2019. "Active learning in automated text classification: a case study exploring bias in predicted model performance metrics," Environment Systems and Decisions, Springer, vol. 39(3), pages 269-280, September.
    3. Domenico C. Amodeo & Royce A. Francis, 2019. "Investigating adoption patterns of residential low impact development (LID) using classification trees," Environment Systems and Decisions, Springer, vol. 39(3), pages 295-306, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arun Varghese & George Agyeman-Badu & Michelle Cawley, 2020. "Deep learning in automated text classification: a case study using toxicological abstracts," Environment Systems and Decisions, Springer, vol. 40(4), pages 465-479, December.
    2. Bokolo Anthony Jnr & Sobah Abbas Petersen, 2023. "Using an extended technology acceptance model to predict enterprise architecture adoption in making cities smarter," Environment Systems and Decisions, Springer, vol. 43(1), pages 36-53, March.
    3. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    4. Bokolo Anthony, 2023. "Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy," Environment Systems and Decisions, Springer, vol. 43(3), pages 453-471, September.
    5. Arun Varghese & Kasey Allen & George Agyeman-Badu & Jennifer Haire & Rebecca Madsen, 2022. "Extraction of mitigation-related text from Endangered Species Act documents using machine learning: a case study," Environment Systems and Decisions, Springer, vol. 42(1), pages 63-74, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:envsyd:v:39:y:2019:i:3:d:10.1007_s10669-019-09738-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.