Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
Ming-Jie Zhao and Adam Pocock and Gavin Brown and Mikel Lujn

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.pdf 706.10kB
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@article{13:2,author={Gavin Brown and Adam Pocock and Ming-Jie Zhao and Mikel Lujn}, Title={Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/brown12a/brown12a.pdf}}
Citation:
Zhao, M., Pocock, A., Brown, G., & Lujn, M.. (2014). Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection [Data set]. Academic Torrents. https://academictorrents.com/details/2c623a098b9f668b9501b3606ab5f94034d81396
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