Weijian Cheng

  • Citations Per Year
Learn More
Least square support vector machine (LS-SVM) has become an effective tool in nonlinear function estimation. But it is a hard optimization problem to determine kernel parameters for LS-SVM owing to its implicit form and numerous local optima. A reliable and efficient hybrid PSO algorithm named self-adaptive lattice PSO with chaotic operator (short for cPSO)(More)
While training an LS-SVM model, two main challenges are parameter optimization and input feature extraction. The main purpose of this article is to address these two problems. Commonly used tools are PSO and BPSO, but they are not suitable for the optimization issues of many local optima owing to its random numbers used to update velocities. In this paper,(More)
Red tides pose a significant environmental and economic threat in the Gulf of Mexico. Timely detection of red tides is important for understanding this phenomenon. In this paper, learning approaches based on k-nearest neighbors, random forests and support vector machines have been evaluated for red tide detection from MODIS satellite images. Detection(More)
  • 1