Difference between revisions of "Active Learning"
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| − | 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. 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] | ||
Revision as of 07:57, 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.
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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.
