Qing Zhou, Fenglu Liao, Chao Mou, Ping Wang. Measuring Interpretability for Different Types of Machine Learning Models. In Mohadeseh Ganji, Lida Rashidi, Benjamin C. M. Fung, Can Wang 0005, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers. Volume 11154 of Lecture Notes in Computer Science, pages 295-308, Springer, 2018. [doi]
@inproceedings{ZhouLMW18,
title = {Measuring Interpretability for Different Types of Machine Learning Models},
author = {Qing Zhou and Fenglu Liao and Chao Mou and Ping Wang},
year = {2018},
doi = {10.1007/978-3-030-04503-6_29},
url = {https://doi.org/10.1007/978-3-030-04503-6_29},
researchr = {https://researchr.org/publication/ZhouLMW18},
cites = {0},
citedby = {0},
pages = {295-308},
booktitle = {Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers},
editor = {Mohadeseh Ganji and Lida Rashidi and Benjamin C. M. Fung and Can Wang 0005},
volume = {11154},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
isbn = {978-3-030-04503-6},
}