Image denoising via solution paths |
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Authors: | Li Wang Ji Zhu |
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Institution: | (1) Institute of Geomatics and Analysis of Risk, University of Lausanne, 1015 Lausanne, Switzerland |
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Abstract: | In this paper, we propose a two-step kernel learning method based on the support vector regression (SVR) for financial time
series forecasting. Given a number of candidate kernels, our method learns a sparse linear combination of these kernels so
that the resulting kernel can be used to predict well on future data. The L
1-norm regularization approach is used to achieve kernel learning. Since the regularization parameter must be carefully selected,
to facilitate parameter tuning, we develop an efficient solution path algorithm that solves the optimal solutions for all
possible values of the regularization parameter. Our kernel learning method has been applied to forecast the S&P500 and the
NASDAQ market indices and showed promising results. |
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Keywords: | |
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