Classification K-nearest neighbor classification D istance functions - PowerPoint PPT Presentation

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

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 separability
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
Feature map
Linear classifier
Linear classifier
Good and bad l inear classifiers
Support Vector Machine Two popular implementations
Margin
Margin
Linear Support Vector Machine
Inseparable case
Linear SVM

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