| Random decision forests TK Ho Proceedings of 3rd international conference on document analysis and …, 1995 | 9770 | 1995 |
| The random subspace method for constructing decision forests TK Ho IEEE transactions on pattern analysis and machine intelligence 20 (8), 832-844, 1998 | 8220 | 1998 |
| Decision combination in multiple classifier systems TK Ho, JJ Hull, SN Srihari IEEE transactions on pattern analysis and machine intelligence 16 (1), 66-75, 1994 | 2348 | 1994 |
| Complexity measures of supervised classification problems TK Ho, M Basu IEEE transactions on pattern analysis and machine intelligence 24 (3), 289-300, 2002 | 863 | 2002 |
| Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language J Hao, TK Ho Journal of Educational and Behavioral Statistics 44 (3), 348-361, 2019 | 419 | 2019 |
| The random subspace method for constructing decision forests. Pattern Analysis and Machine Intelligence TK Ho IEEE Transactions on 20 (8), 832-844, 1998 | 408 | 1998 |
| Nearest neighbors in random subspaces TK Ho Advances in Pattern Recognition: Joint IAPR International Workshops SSPR'98 …, 1998 | 301 | 1998 |
| How complex is your classification problem? a survey on measuring classification complexity AC Lorena, LPF Garcia, J Lehmann, MCP Souto, TK Ho ACM Computing Surveys (CSUR) 52 (5), 1-34, 2019 | 279 | 2019 |
| A data complexity analysis of comparative advantages of decision forest constructors TK Ho Pattern Analysis & Applications 5, 102-112, 2002 | 267 | 2002 |
| Data complexity in pattern recognition M Basu, TK Ho Springer Science & Business Media, 2006 | 239 | 2006 |
| Multiple classifier combination: Lessons and next steps TK Ho Hybrid methods in pattern recognition, 171-198, 2002 | 224 | 2002 |
| SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals P Mirowski, TK Ho, S Yi, M MacDonald International Conference on Indoor Positioning and Indoor Navigation, 1-10, 2013 | 220 | 2013 |
| Methods and apparatus for location determination based on dispersed radio frequency tags MR Andrews, TK Ho, GP Kochanski, LJ Lanzerotti, DJ Thomson US Patent 6,900,762, 2005 | 192 | 2005 |
| A sparse coding approach to household electricity demand forecasting in smart grids CN Yu, P Mirowski, TK Ho IEEE Transactions on Smart Grid 8 (2), 738-748, 2016 | 187 | 2016 |
| Proceedings of 3rd international conference on document analysis and recognition TK Ho Proceedings of 3rd International Conference on Document Analysis and Recognition, 1995 | 161 | 1995 |
| Design of the 2015 chalearn automl challenge I Guyon, K Bennett, G Cawley, HJ Escalante, S Escalera, TK Ho, N Macià, ... 2015 International joint conference on neural networks (IJCNN), 1-8, 2015 | 157 | 2015 |
| Building projectable classifiers of arbitrary complexity TK Ho, EM Kleinberg Proceedings of 13th International Conference on Pattern Recognition 2, 880-885, 1996 | 146 | 1996 |
| Domain of competence of XCS classifier system in complexity measurement space E Bernadó-Mansilla, TK Ho IEEE Transactions on Evolutionary Computation 9 (1), 82-104, 2005 | 138 | 2005 |
| Demand forecasting in smart grids P Mirowski, S Chen, TK Ho, CN Yu Bell Labs technical journal 18 (4), 135-158, 2014 | 127 | 2014 |
| Documentation for the data complexity library in c++ A Orriols-Puig, N Macia, TK Ho Universitat Ramon Llull, La Salle 196 (1-40), 12, 2010 | 116 | 2010 |
