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Jan 11, 2024 •162 likes •395 views

Classification K-nearest neighbor classification D istance functions Choice of k Choice of k Leave-one-out cross validation K-fold cross validation Classification Error = Average classification error on K folds Linear Classification Linear

  1. Classification

  3. K-nearest neighbor classification

  4. D istance functions

  5. Choice of k

  6. Choice of k

  8. Leave-one-out cross validation

  9. K-fold cross validation Classification Error = Average classification error on K folds

  10. Linear Classification

  11. Linear separability

  12. Inseparability • Real world problems: there may not exist a hyperplane that separates cleanly • Solution to this “inseparability” problem: map data to higher dimensional space • Called the “feature space”, as opposed to the original “input space” • Inseparable training set can be made separable with proper choice of feature space

  13. Feature map

  14. Linear classifier

  15. Linear classifier

  16. Good and bad l inear classifiers

  17. Support Vector Machine Two popular implementations

  18. Margin

  19. Margin

  20. Linear Support Vector Machine

  21. Inseparable case

  22. Linear SVM

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