KernelMethods in Medical Imaging ぱらぱらめくる『Handbook of Biomedical Imaging Methodologies and Clinical Research』

  • 機械学習を使う。サポートベクターマシンカーネル・リッジ・回帰、カーネルPCA
  • Machine learning with kernels
    • Basics
      • Classification and Regression
      • Loss function
      • Duality between features and similarity measures
      • Optimization problem over functions
    • Kernels
      • Kernels as inner products in the feature space
      • Geometric interpretation and kernel trick
      • Reproducing kernels as feature maps
      • Gaussian case
      • The overfitting problem
      • Kernels as regularizers
    • Kernelization of existing linear algorithms
      • A very simple hyperplanar classifier
      • Principal component analysis
      • kernel ridge regression and the representer theorem
    • Support vectors
      • Hyperplanar classifier in feature space and margin
      • Soft margin
      • Lagrangian approach and dual problem
      • Support vector machine
  • Application to Intermodality Image Prediction
    • The MR-CT issue
    • Atlas registration vs. patches
    • Image prediction using kernel methods
    • Local learning