Software (all in Matlab)


·         Recursive Autonomy Identification (RAI) algorithm (JMLR, 10:1527-1570, 2009)

o   RAI is a zip file that contains a prototype of the RAI implementation and a usage file.

o   test_RAI is a zip file that contains a demo file (test_struct_learn_RAI.m) to test RAI on the Alarm network, along with an Alarm 10000-sample dataset. It is the same dataset used in the RAI-usage file. The set was generated using the original mk_alarm_bnet in BNT file (path: "...\bnt\BNT\examples\static\Models"). For different dataset sizes or other datasets, a more optimal threshold might be needed, and that threshold can be identified using the method given in the usage file.

o   test_struct_learn_RAI is a file that outputs the results of running test_struct_learn_RAI.m in Matlab.

·         Risk Minimization by Cross-Validation (RMCV) algorithm (IJAR, 53:248-272, 2012)

o   A readme file

o   A demo file

o   The codes for the RMCV score and algorithm

·         The Bayesian ARTMAP (IEEE TNN, 18(6):1628-1644, 2007)

o   A user guide

o   The code

o   A defaults file

o   An example (see the user guide)

·         Adaptive thresholding in structure learning of a Bayesian network (IJCAI2013, pp. 1458-1464, 2013) (software to appear shortly)

·         Learning Pairwise Cluster Comparison (LPCC)

o   A preliminary version (ACML2012, JMLR Workshop & Conference Proceedings, 25:33-48, 2012)

o   The full paper: Part I – Theory and overview (JMLR, 17(233):1-52, 2016)

o   The full paper: Part II – Algorithm and evaluation (JMLR, 17(224):1-45, 2016)

o   Version 1 code (12 December, 2014)

o   Version 2 code (30 June, 2015)

o   Version 3 code and data sets (31 December, 2016)

o   A readme file

·         Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns (ESANN2017)

o   Readme

o   Zip file with the code

·         Identifying Human Lifestyles by Trajectory Embedding from Cellular Data

o   Readme

o   Zip file with code

·         Identifying and Predicting Social Lifestyles in People's Trajectories by Neural Networks

o   Readme

o   Zip file with the code


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