| Chainer: a next-generation open source framework for deep learning S Tokui, K Oono, S Hido, J Clayton Proceedings of workshop on machine learning systems (LearningSys) in the …, 2015 | 918 | 2015 |
| A least-squares approach to direct importance estimation T Kanamori, S Hido, M Sugiyama The Journal of Machine Learning Research 10, 1391-1445, 2009 | 416 | 2009 |
| Roughly balanced bagging for imbalanced data S Hido, H Kashima, Y Takahashi Statistical Analysis and Data Mining: The ASA Data Science Journal 2 (5‐6 …, 2009 | 220 | 2009 |
| Statistical outlier detection using direct density ratio estimation S Hido, Y Tsuboi, H Kashima, M Sugiyama, T Kanamori Knowledge and information systems 26 (2), 309-336, 2011 | 197 | 2011 |
| Direct density ratio estimation for large-scale covariate shift adaptation Y Tsuboi, H Kashima, S Hido, S Bickel, M Sugiyama Journal of Information Processing 17, 138-155, 2009 | 138 | 2009 |
| A linear-time graph kernel S Hido, H Kashima 2009 Ninth IEEE International Conference on Data Mining, 179-188, 2009 | 113 | 2009 |
| Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection T Kanamori, S Hido, M Sugiyama Advances in neural information processing systems, 809-816, 2008 | 92 | 2008 |
| Inlier-based outlier detection via direct density ratio estimation S Hido, Y Tsuboi, H Kashima, M Sugiyama, T Kanamori 2008 Eighth IEEE international conference on data mining, 223-232, 2008 | 84 | 2008 |
| Unsupervised change analysis using supervised learning S Hido, T Idé, H Kashima, H Kubo, H Matsuzawa Pacific-Asia Conference on Knowledge Discovery and Data Mining, 148-159, 2008 | 77 | 2008 |
| Cupy: A numpy-compatible library for nvidia gpu calculations R Okuta, Y Unno, D Nishino, S Hido, C Loomis Proceedings of Workshop on Machine Learning Systems (LearningSys) in The …, 2017 | 59 | 2017 |
| A density-ratio framework for statistical data processing M Sugiyama, T Kanamori, T Suzuki, S Hido, J Sese, I Takeuchi, L Wang IPSJ Transactions on Computer Vision and Applications 1, 183-208, 2009 | 49 | 2009 |
| AMIOT: induced ordered tree mining in tree-structured databases S Hido, H Kawano Fifth IEEE International Conference on Data Mining (ICDM'05), 8 pp., 2005 | 46 | 2005 |
| Jubatus: An open source platform for distributed online machine learning S Hido, S Tokui, S Oda NIPS 2013 Workshop on Big Learning, Lake Tahoe, 2013 | 38 | 2013 |
| Technique for classifying data S Hido US Patent 9,218,572, 2015 | 24 | 2015 |
| System for inspecting information processing unit to which software update is applied S Hido, S Munetoh, S Suzuki, N Uramoto, S Yoshihama US Patent 8,887,146, 2014 | 22 | 2014 |
| Time series data adaptation and sensor fusion systems, methods, and apparatus JB Clayton, D Okanohara, S Hido US Patent 10,410,113, 2019 | 19 | 2019 |
| Machine learning heterogeneous edge device, method, and system D Okanohara, JB Clayton, T Nishikawa, S Hido, N Kubota, N Ota, S Tokui US Patent 9,990,587, 2018 | 19 | 2018 |
| Increasing Availability of an Industrial Control System K Hamzaoui, S Hido, S Suzuki, S Yoshihama US Patent App. 13/365,626, 2012 | 18 | 2012 |
| Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment D Okanohara, JB Clayton, T Nishikawa, S Hido, N Kubota, N Ota, S Tokui US Patent 10,387,794, 2019 | 16 | 2019 |
| Information identification method, program product, and system using relative frequency S Hido, M Tatsubori US Patent 9,471,882, 2016 | 15 | 2016 |
