CVonline: Visual Learning

  1. Behavior Learning
    1. Discrete
    2. Probabilistic
  2. Geometric Feature Learning
  3. Learning Technologies
    1. Bayesian / Probabilistic Model Learning
      1. Bayesian Principal Component Analysis (See Principal Component and Related Approaches)
      2. Latent Variable Methods
      3. Variational Bayes
    2. Clustering
      (See also Classifiers and Distance Metrics)
      1. Fuzzy
      2. Hierarchical
      3. K-Means
      4. Mean-shift
      5. Neural Gas Clustering
    3. Parametric and Non-Parametric
    4. Pattern Matrices
    5. Proximity Matrices
    6. Self-Organizing Feature Maps/Kohonen Networks
  4. EM: Expectation Maximization
  5. Ensemble methods
    1. Bagging
    2. Boosting
    3. Extremely Random Trees (Extra-trees)
    4. Random Forests
    5. Vector Boosting
  6. Feature Selection
  7. Genetic Algorithms/Genetic Programming (See Genetic Algorithms/Programming)
  8. Neural Networks (See Neural Networks)
  9. Principal Component Analysis (See Principal Component and Related Approaches)
  10. Support Vector Machines
  11. Semi-Supervised Learning
  12. Vector Quantization
  • Shape Model Learning
    1. Range Data Fusion
    2. Space Carving
    3. Structural Learning
      1. Architectural Models
    4. Volumetric Model Recovery
    5. Voxel Coloring
  • Property Learning
    1. Spatial-Temporal Patterns

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