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Types of Learning

Learning with Different Output Space \(\gamma\)

Binary classification

  • Y = {−1, +1}

  • binary classification

Multiclass Classification

  • classify US coins (1c, 5c, 10c, 25c) by (size, mass)
  • \(\gamma = {1c,5c,10c,25c}\),or \(\gamma = {1,2,··· ,K}\) (abstractly)
  • binary classification: special case with K =2
  • multiclass.png

Regression

  • \(\gamma = \mathbb{R}\) or \(\gamma = [lower, upper] \subset \mathbb{R}\) (bounded regression)

Structured Learning: Sequence Tagging Problem

  • a fancy but complicated learning problem
  • sentence -> structure (class of each word)
  • \(\gamma = \{PVN,PVP,NVN,PV,···\}\), not including VVVVV
  • huge multiclass classification problem (\(structure \equiv hyperclass\)) without explicit class definition

Learning with Different Data Label \(y_n\)

Supervised learning

  • every \(x_n\) comes with corresponding \(y_n\)

Unsupervised learning

  • clustering
    • articles -> topics
    • consumer profiles -> consumer groups
  • density estimation: {xn} -> density(x)
    • i.e. traffic reports with location -> dangerous areas
  • outlier detection: {xn} -> unusual(x)
    • i.e. Internet logs -> intrusion alert

Semi-supervised learning

  • leverage unlabeled data to avoid expensive labeling

Reinforcement Learning

  • Teach Your Dog: Say Sit Down
    • cannot easily show the dog that \(y_n\) = sit when \(x_n\) = sit down
    • but can punish to say \(\hat{y_n}\) = pee is wrong
    • but can reward to say \(\hat{y_n}\) = sit is good
  • learn with partial/implicit information (often sequentially)

Learning with different Protocol \(f \rightarrow (x_n,y_n)\)

Batch Learning

  • batch supervised multiclass classification: learn from all known data

Online Learning:

  • hypothesis improves through receiving data instances sequentially

Active Learning: Learning by ‘Asking’

  • improve hypothesis with fewer labels (hopefully) by asking questions strategically

Learning with different Input Space \(\chi\)

  • concrete features: each dimension of \(\chi \in \mathbb{R}\) represents sophisticated physical meaning
  • Raw Features
    • simple physical meaning; thus more difficult for ML than concrete features
    • often need human or machines to convert to concrete ones
  • Abstract Features: again need feature conversion/extraction/construction