Abstract
Regularization and finding optimal solution for the classification problems are well known issue in the machine learning, but most of researches have been separately studied or considered as a same problem about these two issues. However, it is obvious that these approaches are not always possible because the evaluation of the performance in classification problems is mostly based on the data distribution and learning methods; therefore this paper suggests a new approach to simultaneously deal with finding optimal regularization parameter and solution in classification and regression problems by introducing dynamically rescheduled momentum with modified SVM in kernel space.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, EM., Jeong, J.C., Lee, BH. (2006). A New Approach for Finding an Optimal Solution and Regularization by Learning Dynamic Momentum. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_4
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DOI: https://doi.org/10.1007/11785231_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35748-3
Online ISBN: 978-3-540-35750-6
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