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readme.md add: readme Jan 5, 2018

readme.md

Stratified Transfer Learning

This directory contains the code and the datasets for Stratified Transfer Learning (STL) proposed in the paper Stratified Transfer Learning for Cross-domain Activity Recognition.

Setup

The code is developed in Matlab. Please use Matlab version 2015+ in case there are some errors. The datasets are also packed in .mat file.

About the code

  • demo.mat is a demo to run the algorithm.

  • STL.mat is the STL algorithm. You are free to tailor it.

    • Since the algorithm is developed more than one year ago and the paper has been rejected several times, I have somewhat lost the patience in developing or maintaining it. Maybe this code is not smooth nor that right, but I'm sure you can find your own way to recitify it.
  • base directory contains some packaged classifier and preprocessing codes. If you want to use SVM, make sure libsvm installed. Then rename svmtrain and svmpredict with libsvmtrain and libsvmpredict, respectively.

  • Other than activity recognition, STL algorithm can also be used in other transfer learning applications such as computer vision and text classification (not tested yet).

About the datasets

The datasets are a little big large that are best not to posted on Github. Please download them alternatively in the following addresses:

Reference

Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, and Philip S. Yu. Stratified Transfer Learning for Cross-domain Activity Recognition. 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).

or in bibtex style:

@inproceedings{wang2018stratified,
    title={Stratified Transfer Learning for Cross-domain Activity Recognition},
    author={Wang, Jindong and Chen, Yiqiang and Hu, Lisha and Peng, Xiaohui and Yu, Philip S},
    booktitle={2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)},
    year={2018}
}

Contact

Feel free to contact jindongwang@outlook.com.