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
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