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bsoid_mdl.md

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BSOID_MDL.m

Purpose: Build a support vector machine classifier using f_10fps and grp outputs from bsoid_gmm.m. This allows future prediction of user defined groups. The algorithm defaults at using the clusters from GMM, but user can also input the merged groups and have a classifier built to their desires. However, we would argue against building a classifier based on human perception of what is the same behavior; instead, build this classifier based on the x number of GMM clusters, and merge the predicted labels later.

function [OF_mdl,CV_amean,CV_asem,acc_fig] = bsoid_mdl(f_10fps,grp,hldout,cv_it,btchsz)

Prior to Usage

Run bsoid_gmm.md first

Inputs to BSOID_MDL.m

  • F_10FPS Compiled features that were used to cluster, 10fps temporal resolution.

  • GRP Statistically different groups of actions based on data. Output is 10Hz.

  • HLDOUT Percentage of data randomly held out for test. Default is 0.20 (80/20 training/testing).

  • CV_IT Number of times to run cross-validation on. Default is 100.

  • BTCHSZ Batch size for randsampling, make sure the hold out data is <= CV_IT*BTCHSZ. Default is 200.

  • IT The number of random initialization for Gaussian Mixture Models. This attempts to find global optimum, instead of local optimum. Default is 20.

Outputs of BSOID_MDL.m

  • OF_MDL Support Vector Machine Classifier Model.

  • CV_AMEAN Cross-validated accuracy mean.

  • CV_ASEM Cross-validated accuracy standard error fo the mean.

  • ACC_FIG Box plot showing classifier performance with individual data points representing a randomly subsampled test set from the hold out portion.

Upon obtaining the outputs

Run bsoid_svm.md next