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Derivative Free Stochastic Discrete Gradient Method with Adaptive Mutation

  • Ranadhir Ghosh
  • Moumita Ghosh
  • Adil Bagirov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)

Abstract

In data mining we come across many problems such as function optimization problem or parameter estimation problem for classifiers for which a good learning algorithm for searching is very much necessary. In this paper we propose a stochastic based derivative free algorithm for unconstrained optimization problem. Many derivative-based local search methods exist which usually stuck into local solution for non-convex optimization problems. On the other hand global search methods are very time consuming and works for only limited number of variables. In this paper we investigate a derivative free multi search gradient based method which overcomes the problems of local minima and produces global solution in less time. We have tested the proposed method on many benchmark dataset in literature and compared the results with other existing algorithms. The results are very promising.

Keywords

Descent Direction Nonsmooth Optimization Adaptive Mutation Discrete Gradient Local Optimization Method 
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

  • Ranadhir Ghosh
    • 1
  • Moumita Ghosh
    • 1
  • Adil Bagirov
    • 1
  1. 1.School of Information Technology and Mathematical SciencesUniversity of BallaratBallaratAustralia

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