English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 93861/124308 (76%)
Visitors : 28926587      Online Users : 610
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
    政大機構典藏 > 商學院 > 統計學系 > 會議論文 >  Item 140.119/111675
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111675

    Title: Data driven geometry for learning
    Authors: 周珮婷
    Chou, Elizabeth P.
    Contributors: 統計系
    Keywords: Artificial intelligence;Biomarkers;Classification (of information);Data mining;Geometry;Learning systems;Microarrays;Pattern recognition;Supervised learning;Trees (mathematics);BiDCG;Complexity penalties;Data clouds;Dimension-reduction;High dimensionality;Microarray data sets;Pattern information;Semi- supervised learning;Learning algorithms
    Date: 2015-07
    Issue Date: 2017-08-08 16:59:37 (UTC+8)
    Abstract: High dimensional covariate information provides a detailed description of any individuals involved in a machine learning and classification problem. The inter-dependence patterns among these covariate vectors may be unknown to researchers. This fact is not well recognized in classic and modern machine learning literature; most model-based popular algorithms are implemented using some version of the dimensionreduction approach or even impose a built-in complexity penalty. This is a defensive attitude toward the high dimensionality. In contrast, an accommodating attitude can exploit such potential inter-dependence patterns embedded within the high dimensionality. In this research, we implement this latter attitude throughout by first computing the similarity between data nodes and then discovering pattern information in the form of Ultrametric tree geometry among almost all the covariate dimensions involved. We illustrate with real Microarray datasets, where we demonstrate that such dual-relationships are indeed class specific, each precisely representing the discovery of a biomarker. The whole collection of computed biomarkers constitutes a global feature-matrix, which is then shown to give rise to a very effective learning algorithm. © Springer International Publishing Switzerland 2015.
    Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9166, 395-402
    11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015; Hamburg; Germany; 20 July 2015 到 21 July 2015; 代碼 156629
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1007/978-3-319-21024-7_27
    DOI: 10.1007/978-3-319-21024-7_27
    Appears in Collections:[統計學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    10.1007_978-3-319-21024-7_27.pdf126KbAdobe PDF186View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

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