Tutorials

• Regression and classification
  • Tutorial 1: Basic regression
  • Tutorial 2: K-fold cross-validation, non i.i.d. data
  • Tutorial 3: Binary classification and Area under ROC curve
  • Tutorial 4: Reduced set approximation
  • Tutorial 5: Multi-target learning
  • References
• Learning to rank
  • Tutorial 1: Ordinal regression
  • Tutorial 2: Query-structured data
  • Tutorial 3: Learning from pairwise preferences
  • Precomputed kernels, reduced set approximation
  • References
• Feature selection
  • Basics of feature selection
  • Feature selection with MNIST
  • References
• Pairwise (dyadic) data, transfer- and zero-shot learning
  • Tutorial 1: KronRLS
  • Tutorial 2: TwoStepRLS, cross-validation with bipartite network
  • Tutorial 2: TwoStepRLS, cross-validation with homogenous network
  • Tutorial 3: CGKronRLS, incomplete data
  • References
• Kernels
  • Tutorial 1: Basic usage
  • Tutorial 2 Precomputed kernels
  • Tutorial 3 Kronecker learners
• Prediction
  • Tutorial 1: Linear predictor
  • Tutorial 2: Non-linear predictor
  • Tutorial 3: Reduced set approximation
  • Tutorial 4: Pairwise predictors
  • Saving the predictor
• Performance measures
  • Tutorial 1: Basic usage
  • Tutorial 2: Multi-class accuracy
• Sparse large data sets and linear models
  • Data set
  • Experiments