English  |  正體中文  |  简体中文  |  Items with full text/Total items : 94286/110023 (86%)
Visitors : 21693156      Online Users : 837
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
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8794


    Title: An improved SVM algorithm based on normalization and Liu-transformation
    Authors: Liu, Hsiang-Chuan;Chiu, Ya-Ching;Liao, Chien-Hsiung;Liu, Tung-Sheng
    Contributors: Department of Bioinformatics
    Keywords: Algorithms;Classifiers;Feature extraction;Ketones;Learning systems;Pattern recognition;Support vector machines;Wavelet analysis;Wavelet transforms;Cross validations;Leave one outs;Liu-transformation;Normalization algorithms;NWFE-transformation;Real datums;Support vector machine classifiers;SVM;SVM algorithms;Transformation algorithms
    Date: 2008
    Issue Date: 2010-04-08 12:06:10 (UTC+0)
    Publisher: Asia University
    Abstract: The support vector machine (SVM) classifier is a popular and appealing classifier .It could be improved by taking some transformation about the original data before classification even sometimes its performance is not good,. In our previous paper, two transformations, NWFE-Transformation and Liu-Transformation are considered. The results showed that the SVM with our Liu-Transformation algorithm has the best performance. In this paper, we considered the further improved SVM algorithm based on not only the Liu- transformation but also the well known normalization, For evaluating the performances of the SVM without any transformation and normalization, the SVM with NWFE-Transformation and Liu-Transformation, respectively, the SVM with one of above two transformations and the well known normalization, a real data experiment by using 5-fold and Leave-one-out Cross-Validation accuracy is conducted. Experimental result shows that the SVM with the proposed Liu-Transformation algorithm and the well known normalization algorithm has the best performance. ©2008 IEEE.
    Relation: Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2 :470-473
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

    Files in This Item:

    File Description SizeFormat
    0KbUnknown540View/Open
    225.doc31KbMicrosoft Word456View/Open


    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