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SVM—History and Applications

  • Vapnik and colleagues (1992)—groundwork from Vapnik & Chervonenkis’ statistical learning theory in 1960s
  • Features: training can be slow but accuracy is high owing to their ability to model complex nonlinear decision boundaries (margin maximization)
  • Used for: classification and numeric prediction
  • Applications:
    • handwritten digit recognition, object recognition, speaker identification, benchmarking time-series prediction tests

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