Title page for etd-0805109-121651


[Back to Results | New Search]

URN etd-0805109-121651
Author Chi-wei Huang
Author's Email Address kiwi@water.ee.nsysu.edu.tw
Statistics This thesis had been viewed 5601 times. Download 3368 times.
Department Electrical Engineering
Year 2008
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A Multiple-Kernel Support Vector Regression
Approach for Stock Market Price Forecasting
Date of Defense 2009-07-22
Page Count 60
Keyword
  • SMO
  • multiple-kernel learning
  • support vector regression
  • Stock market forecasting
  • gradient projection
  • Abstract Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method.
    By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.
    Advisory Committee
  • Tzung-Pei Hong - chair
  • Wen-Yang Lin - co-chair
  • Chaur-Heh Hsieh - co-chair
  • Chung-Ming Kuo - co-chair
  • Shie-Jue Lee - advisor
  • Files
  • etd-0805109-121651.pdf
  • indicate access worldwide
    Date of Submission 2009-08-05

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys