Murat C. Ganiz, Ph.D.
Affiliations: | 2008 | Lehigh University, Bethlehem, PA, United States |
Area:
Computer ScienceGoogle:
"Murat Ganiz"Parents
Sign in to add mentor| William M. Pottenger | grad student | 2008 | Lehigh University | |
| (Higher -order path analysis for supervised machine learning.) | ||||
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Publications
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| Bayrak G, Toprak MŞ, Ganiz MC, et al. (2022) Deep Learning-Based Brain Hemorrhage Detection in CT Reports. Studies in Health Technology and Informatics. 294: 866-867 |
| Altınel B, Ganiz MC. (2018) Semantic text classification: A survey of past and recent advances Information Processing and Management. 54: 1129-1153 |
| Altınel B, Ganiz MC, Diri B. (2017) Instance labeling in semi-supervised learning with meaning values of words Engineering Applications of Artificial Intelligence. 62: 152-163 |
| Altınel B, Ganiz MC. (2016) A new hybrid semi-supervised algorithm for text classification with class-based semantics Knowledge-Based Systems. 108: 50-64 |
| Ganiz MC, Tutkan M, Akyokus S. (2015) A novel classifier based on meaning for text classification Inista 2015 - 2015 International Symposium On Innovations in Intelligent Systems and Applications, Proceedings |
| Altinel B, Diri B, Ganiz MC. (2015) A novel semantic smoothing kernel for text classification with class-based weighting Knowledge-Based Systems. 89: 265-277 |
| Altınel B, Ganiz MC, Diri B. (2015) A corpus-based semantic kernel for text classification by using meaning values of terms Engineering Applications of Artificial Intelligence. 43: 54-66 |
| Altinel B, Ganiz MC, Diri B. (2014) A simple semantic kernel approach for SVM using higher-order paths Inista 2014 - Ieee International Symposium On Innovations in Intelligent Systems and Applications, Proceedings. 431-435 |
| Tutkan M, Ganiz MC, Akyokuş S. (2014) Helmholtz principle based supervised and unsupervised feature selection methods for text mining Information Processing and Management |
| Poyraz M, Kilimci ZH, Ganiz MC. (2014) Higher-order smoothing: A novel semantic smoothing method for text classification Journal of Computer Science and Technology. 29: 376-391 |