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Semipolynomial kernel optimization based on the fisher method

Taghizadeh, E ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/MLSP.2011.6064561
  3. Abstract:
  4. Kernel based methods are significantly important in the pattern classification problem, especially when different classes are not linearly separable. In this paper, we propose a new kernel, which is the modified version of the polynomial kernel. The free parameter (d) of the proposed kernel considerably affects the error rate of the classifier. Thus, we present a new algorithm based on the Fisher criterion to find the optimum value of d. Simulation results show that using the proposed kernel for classification leads to satisfactory results. In our simulation in most cases the proposed method outperforms the classification using the polynomial kernel
  5. Keywords:
  6. Pattern classification ; Error rate ; Fisher criterion ; Fisher method ; Free parameters ; Kernel based methods ; Kernel learning ; Kernel optimizations ; Linearly separable ; Optimum value ; Pattern classification problems ; polynomial kernel ; Polynomial kernels ; Learning systems ; Pattern recognition ; Signal processing ; Polynomials
  7. Source: IEEE International Workshop on Machine Learning for Signal Processing, 18 September 2011 through 21 September 2011 ; September , 2011 , Page(s): 1 - 6 ; 9781457716232 (ISBN)
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6064561