@article{epos1329,
          volume = {30},
          number = {1},
          author = {Zhi-xiang Zhou and Feng-qing Han},
           title = {An iterative modified kernel based on training data},
       publisher = {Springer Verlag},
         journal = {Applied Mathematics and Mechanics},
           pages = {121--128},
            year = {2009},
        keywords = {support vector regression, data-dependent, kernel function, iteration},
             url = {https://episodesplatform.eu/eprints/1329/},
        abstract = {To improve performance of a support vector regression, a new method for a modi?ed kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modi?ed repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Simulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration
number, the ?gure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.}
}