A New Approach for Finding an Optimal Solution and Regularization by Learning Dynamic Momentum

  • Eun-Mi Kim
  • Jong Cheol Jeong
  • Bae-Ho Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


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.


Support Vector Machine Regularization Parameter Little Mean Square Kernel Method Kernel Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eun-Mi Kim
    • 1
  • Jong Cheol Jeong
    • 2
  • Bae-Ho Lee
    • 1
  1. 1.Dept. of Computer EngineeringChonnam National UniversityRepublic of Korea
  2. 2.Dept. of Electrical Engineering & Computer ScienceThe University of KansasUSA

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