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Machine learning with Weka for Kung fu - training purposes

Machine learning with Weka for Kung fu - training purposes. Victoria Værnø victoriavarno@gmail.com v aernoe.wordpress.com. Background. Motion capture and machine learning workbenches are accessible to the public .

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Machine learning with Weka for Kung fu - training purposes

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  1. Machine learning with Weka for Kung fu-training purposes Victoria Værnø victoriavarno@gmail.com vaernoe.wordpress.com

  2. Background Motion capture and machinelearningworkbenchesareaccessible to thepublic. Links betweencheap hardware (eg. web-cams and Kinect) & opensourcemachinelearningsoftware is still hard to find for motion capture.

  3. Research focus • Build a model which is good enough to consider for a beta application for amateur Kung-Fu training purposes. • For separately created unseen data, high accuracy and high “bad”-class label precision • What challenges follow a relatively limited training set and what basic machine learning techniques can reduce these?

  4. Method Meta- Learner Clustering Test Result- and Data Analysis

  5. Data Data attributes: 18 joints * 3 dimensions * 6 frames per movie + 1 classlable = 325 attributes .bvh .arff

  6. Iteration 1 Generate MLP model on the training data. Accuracy 97% Precision • of”Bad” 1 Good! Too good?

  7. Iteration 1 Test the model with unseen dataset. Accuracy 85% Precision • of”Bad” 1 Not so great. Unbalanced dataset?

  8. Unbalanced Data Attributevalueswhich in realitydefineclass 1 Class 1 Class 2 Attributevalueswhichveryoftenarise in class 1, butdoesnotdefineclass 1. Attributevalueswhich in realitydefineclass 2 This part is muchbigger in the test set and real life.

  9. Iteration 2 Clustering K-means K=3

  10. Trying to compensate for unbalanced data • Meta-classifier AdaBoostM1 boosting algorithm • Collecting more data – new people. • Other suggestions, please tell or email me! (victoriavarno@gmail.com)

  11. Iteration 3 Generate models and test on Data set 2. Just MLP + Data set 1 AdaBoostM1 84% AdaBoostM1 + Data set 1 80% Just MLP + Data set 1 + Data set 3 + 89% AdaBoostM1 + Data set 1 + Data set 3 86%

  12. Conclusion • Promisingresultsfor furtherinvestigation. • Cross-validation over 90% • ”Bad” classlabelalwaysprecision = 1 • Adding just one more person -> unseen data 89% • Classifyingunseenpeople’s kicks remainunexplored. • Data collection: Hard for one person to create balanced motion data. • Modeling: Boosting strategy to combat unbalanced motion capture data works in some cases, but adding a different person’s motion is far more efficient. ”Spend time gathering more data rather than tuning a particular method” Nilsson N.J Lessons Learned

  13. Thank You! Q?

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