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A recursive algorithm for parameter optimization in support vector regression

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  1. Abstract
  2. Introduction
  3. Theory of support vector machines
  4. Proposed approach for parameter optimization
  5. Experimental settings
    1. Data set
    2. Data pre-processing
  6. Experimental results
  7. Conclusions
  8. Results

Parameter Optimization is the most crucial task during any model development. The task becomes challenging when the model involves multiple parameters with interrelationships among them. This work concentrates on parameter optimization for Support Vector Regression of atmospheric variables. Support vector regression involves the parameter C which controls the smoothness of the approximating function and epsilon that determines the error tolerance margin. Due to the favorable performance of Radial Basis Function kernel in the previous studies on atmospheric variable regression it is adopted in this study. Hence we require to optimize the parameter gamma (_) in addition to the support vector regression parameters C and epsilon.

[...] ?Support vector regression for realtime flood stage forecasting.? Journal of Hydrology pp 704- Ronan Collobert and Samy Benegio Torch: Support Vector Machines for LargeScale Regression Problems.? Journal of Machine Learning Research pp Smola A.J, and Scholkopf Tutorial on support vector regression,? Neuro COLT Technical Report NC-TR-98-030, Royal Holloway College, Uni of London, UK Stanislaw Osowski and Konrad Garanty, ?Forecasting of daily meteorological pollution using wavelets and support vector machine.? Engineering Applications of Artificial Intelligence pp 745- Wei-Zhen Lu. Wen-Jian Wang. [...]


[...] The proposed algorithm is applied for parameter optimization of support vector regression and the results at various stages are tabulated and shown in figs and 4. Fig 2 shows the variation of Mean Square Error with C and the values are tabulated in table 1. It can be observed that for C = the model gives the best performance with minimal MSE. Figure 3 shows the variation of MSE with whose values are tabulated in table 2. It can be observed that has a significant effect on the performance of the system. [...]

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