Difference between revisions of "Active Learning"
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| − | The code below applies the proposed active learning method to USPS Handwritten Digit Recognition dataset. It is written in MATLAB R2013a. It uses the SGWT toolbox which is also included. To run the code, simply unpack the directory and run main_usps.m. If you have any questions, please email agadde at usc dot edu. | + | The code below applies the proposed active learning method to [http://www.cs.nyu.edu/~roweis/data.html USPS Handwritten Digit Recognition dataset]. It is written in MATLAB R2013a. It uses the [http://wiki.epfl.ch/sgwt SGWT toolbox] which is also included. |
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| + | To run the code, simply unpack the directory and run main_usps.m. If you have any questions, please email agadde at usc dot edu. | ||
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[http://sipi.usc.edu/~ortega/Papers/active-ssl-via-sampling-theory-code.zip Active SSL via Sampling Theory Code] | [http://sipi.usc.edu/~ortega/Papers/active-ssl-via-sampling-theory-code.zip Active SSL via Sampling Theory Code] | ||
Latest revision as of 07:59, 21 January 2015
- A. Gadde, A. Anis and A. Ortega "Active Semi-Supervised Learning Using Sampling Theory for Graph Signals", KDD 2014, New York, USA, 2014.
- A. Anis, A. Gadde and A. Ortega "Towards a Sampling Theorem for Signals on Arbitrary Graphs", ICASSP 2014, Florence, Italy, 2014 (Best student paper award).
- S.K. Narang, A. Gadde and A. Ortega "Signal Processing Techniques for Interpolation of Graph Structured Data", ICASSP 2013, Vancouver, Canada, 2013.
- S.K. Narang, A. Gadde, E. Sanou and A. Ortega "Localized Iterative Methods for Interpolation of Graph Structured Data", GlobalSIP 2013, Austin, USA, 2013.
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The code below applies the proposed active learning method to USPS Handwritten Digit Recognition dataset. It is written in MATLAB R2013a. It uses the SGWT toolbox which is also included.
To run the code, simply unpack the directory and run main_usps.m. If you have any questions, please email agadde at usc dot edu.
