Mathematica Eterna
Open Access

Department of Mathematics, Jiangsu Administration Institute, Nanjing, China

Publications

• Review Article   
  A Clustering-Based Multiple Kernel Learning Algorithm for Multi-Class-Review Article Classification
  Author(s): Zhang Xiaofeng*
  
  Multiple kernel learning algorithms typically optimize kernel alignment, structural risk minimization, and Bayesian functions. However, they have limitations, including inapplicability to multi-class classification, high time complexity, and no analytic solution. Analyzing clustering and classification similarities, we propose a novel Clustering-Based Multiple Kernel Learning (CBMKL) algorithm for multi-class classification. This algorithm transforms input space to high-dimension feature space using multiple kernel mapping functions. It estimates base kernel function weights and constructs the decision function using clustering objectives. This CBMKL algorithm has several advantages. • It handles multi-class problems directly. • This algorithm has an analytical solution, avoiding approximate solutions from sampling methods. • It also has po.. View More»
  

  Abstract PDF