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Presenter : Fen-Rou, Ciou Authors : Toh Koon Charlie Neo, Dan Ventura 2012, PRL

A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric. Presenter : Fen-Rou, Ciou Authors : Toh Koon Charlie Neo, Dan Ventura 2012, PRL. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Fen-Rou, Ciou Authors : Toh Koon Charlie Neo, Dan Ventura 2012, PRL

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  1. A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric Presenter : Fen-Rou, CiouAuthors : Toh Koon Charlie Neo, Dan Ventura2012, PRL

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • The k-nearest neighbor pattern classifier is an effective learning algorithm, it can result in large model sizes.

  4. Objectives • The paper present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting to increase the accuracy and condense the model size.

  5. Methodology - Framework x Dz xi v = {+, } AdaBoost

  6. Methodology Sensitivity data order - Randomize - Batch update

  7. Methodology Sensitivity data order - Randomize - Batch update

  8. Methodology Voting mechanism - simple voting - error-weigh voting

  9. Methodology Condensing model size - optimal weight - average the weight

  10. Experiments

  11. Experiments

  12. Experiments Fig 8. Boosted k-NN with randomized data order. Fig 9. Boosted k-NN with batch update. Fig 11. Boosted k-NN with optimal weights. Fig 10. Boosted k-NN with error-weighted voting. Fig 12. Boosted k-NN with average weights.

  13. Experiments

  14. Conclusions • The Boosted k-NN can boost the generalization accuracy of the k-nearest neighbor algorithm. • The Boosted k-NN algorithm modifier the decision surface, producing a better solution.

  15. Comments • Advantages • The paper describes rich experiment. • Applications • classification

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