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PDMC Entry. By Saurabh Amin Dept. of Civil Eng., UT Austin Presenter Nicholas K. Jong Dept. of Computer Sciences, UT Austin. With help of Gunjan Gupta, Dept. of Elect. & Comp. Eng. UT Austin. What was planned. What was done. Bayesian Prior Model for context identification

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Pdmc entry

PDMC Entry

By

Saurabh Amin

Dept. of Civil Eng., UT Austin

Presenter

Nicholas K. Jong

Dept. of Computer Sciences, UT Austin

With help of

Gunjan Gupta, Dept. of Elect. & Comp. Eng. UT Austin



What was done
What was done

Bayesian Prior Model for context identification

  • Preprocessing

    • Remove all sessions that have no labeled sub-sessions

    • Split 70 for training and 30% for testing

    • Select labeled sub-sessions

    • Assign class labels: Context1, Context2 , Others

  • Model Building

    • Count #(Characteristic 1) for each class label

    • Fit 3-parameter Weibulls. Size and scale parameters fixed by observation and shape parameter (the most critical one) fixed by observing performance on testing set

    • Apply Bayes theorem: Pr(Con|Char)=P(Char|Con)*P(Con)/P(Char)

  • Performance Evaluation

    • Evaluate performance on scoring criterion (fixed by organizers)

    • Compare with the most naïve global prior model: Better performance

      Similar procedure repeated for gender prediction


What is to be done
What is to be done

  • Feature extraction and classification

  • Aggregated log-transformed 32-point FFT plot


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