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Minimum deviation distribution machine for large scale regression
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AI
DOI：10.1016/j.knosys.2018.02.002.png)
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Abstract
En 中文
In this paper, by introducing the statistics of training data into support vector regression (SVR), we propose a minimum deviation distribution regression (MDR). Rather than just minimizing the structural risk, MDR also minimizes both the regression deviation mean and the regression deviation variance, which is able to deal with the different distribution of boundary data and noises. The formulation of minimizing the first and second order statistics in MDR leads to a strongly convex quadratic programming problem (QPP). An efficient dual coordinate descend algorithm is adopted for small sample problem, and an average stochastic gradient algorithm for large scale one. Both theoretical analysis and experimental results illustrate the efficiency and effectiveness of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Regression
Support vector machine
Minimum deviation distribution machine
Dual coordinate descend algorithm
Stochastic gradient algorithm
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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