LIBLINEAR: A Library for Large Linear Classification

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

Distributional Structure

Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807

Towards Unified Object Detection and Semantic Segmentation

Dong, J., Chen, Q., Yan, S., & Yuille, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_20 · Cited by 70

MedGCN: Medication recommendation and lab test imputation via graph convolutional networks

Mao, C., Yao, L., & Luo, Y. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104000 · Cited by 70

Improving random forests by neighborhood projection for effective text classification

Salles, T., Gonçalves, M., Rodrigues, V., & Rocha, L. (2018) · Information Systems · doi:10.1016/j.is.2018.05.006 · Cited by 66

Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech

Krajewski, J., Schnieder, S., Sommer, D., Batliner, A., & Schuller, B. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.12.021 · Cited by 67

Applying active learning to assertion classification of concepts in clinical text

Chen, Y., Mani, S., & Xu, H. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.11.003 · Cited by 57

miRTex: A Text Mining System for miRNA-Gene Relation Extraction

Li, G., Ross, K. E., Arighi, C. N., Peng, Y., Wu, C. H., & Vijay-Shanker, K. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004391 · Cited by 61

Discrete Network Embedding

Shen, X., Pan, S., Liu, W., Ong, Y.-S., & Sun, Q.-S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/493 · Cited by 57

Ensemble method–based extraction of medication and related information from clinical texts

Kim, Y., & Meystre, S. M. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz100 · Cited by 56

A deep learning framework for hybrid heterogeneous transfer learning

Zhou, J. T., Pan, S. J., & Tsang, I. W. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2019.06.001 · Cited by 53

Strength modelling for real-worldautomatic continuous affect recognition from audiovisual signals

Han, J., Zhang, Z., Cummins, N., Ringeval, F., & Schuller, B. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2016.11.020 · Cited by 53

Depression Detection from Short Utterances via Diverse Smartphones in Natural Environmental Conditions

Huang, Z., Epps, J., Joachim, D., & Chen, M. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-1743 · Cited by 52

Feature selection based on tensor decomposition and object proposal for night-time multiclass vehicle detection

Kuang, H., Chen, L., Chan, L. L. H., Cheung, R. C. C., & Yan, H. (2019) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2018.2872891 · Cited by 49

A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text

Roberts, K., Rink, B., & Harabagiu, S. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001619 · Cited by 45

Mobile Traffic Classification Through Physical Control Channel Fingerprinting: A Deep Learning Approach

Trinh, H. D., Fernandez Gambin, A., Giupponi, L., Rossi, M., & Dini, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3028197 · Cited by 40

Cross-domain recommendation with user personality

Wang, H., Zuo, Y., Li, H., & Wu, J. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106664 · Cited by 37

Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection

Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24571-3_7 · Cited by 36

Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Gong, J. J., Naumann, T., Szolovits, P., & Guttag, J. V. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098064 · Cited by 34

iCOP: Live forensics to reveal previously unknown criminal media on P2P networks

Peersman, C., Schulze, C., Rashid, A., Brennan, M., & Fischer, C. (2016) · Digital Investigation · doi:10.1016/j.diin.2016.07.002 · Cited by 33

Predictions of european basketball match results with machine learning algorithms

Lampis, T., Ioannis, N., Vasilios, V., & Stavrianna, D. (2023) · Journal of Sports Analytics · doi:10.3233/jsa-220639 · Cited by 34

Automated prediction of the apnea-hypopnea index using a wireless patch sensor

Selvaraj, N., & Narasimhan, R. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2014.6943981 · Cited by 28

Effects of review spam in a firm-initiated virtual brand community: Evidence from smartphone customers

Feng, N., Su, Z., Li, D., Zheng, C., & Li, M. (2018) · Information & Management · doi:10.1016/j.im.2018.05.012 · Cited by 22

Automatic recognition of self-reported and perceived emotion: does joint modeling help?

Zhang, B., Essl, G., & Mower Provost, E. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2993173 · Cited by 21

Age detection in chat

Tam, J., & Martell, C. H. (2009) · 2009 IEEE International Conference on Semantic Computing · doi:10.1109/icsc.2009.37 · Cited by 21

Combining psycho-linguistic, content-based and chat-based features to detect predation in chatrooms

Barreiro, A., Losada, D. E., & Parapar, J. (2014) · Verlag der Technischen Universität Graz · doi:10.3217/jucs-020-02-0213 · Cited by 16

Self-Tuned Descriptive Document Clustering Using a Predictive Network

Brockmeier, A. J., Mu, T., Ananiadou, S., & Goulermas, J. Y. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2781721 · Cited by 15

Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk

Dima, D. C., Adams, R., Linden, S. C., Baird, A., Smith, J., Foley, S., Perry, G., Routley, B. C., Magazzini, L., Drakesmith, M., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00998-w · Cited by 14

Extracting Human Temporal Orientation from Facebook Language

Schwartz, H. A., Park, G., Sap, M., Weingarten, E., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., Berger, J., Seligman, M., et al. (2015) · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.3115/v1/n15-1044 · Cited by 13

Comparing laboratory and in-the-wild data for continuous Parkinson’s Disease tremor detection

Zhang, A., De la Torre, F., & Hodgins, J. (2020) · 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176255 · Cited by 12

Fast flux discriminant for large-scale sparse nonlinear classification

Chen, W., Chen, Y., & Weinberger, K. Q. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623627 · Cited by 12

Using deep learning to identify translational research in genomic medicine beyond bench to bedside

Hsu, Y.-Y., Clyne, M., Wei, C.-H., Khoury, M. J., & Lu, Z. (2019) · Database · doi:10.1093/database/baz010 · Cited by 6

Virk: an active learning-based system for bootstrapping knowledge base development in the neurosciences

Ambert, K. H., Cohen, A. M., Burns, G. A. P. C., Boudreau, E., & Sonmez, K. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00038 · Cited by 6

A probabilistic automated tagger to identify human-related publications

Cohen, A. M., Dunivin, Z. O., & Smalheiser, N. R. (2018) · Database · doi:10.1093/database/bay079 · Cited by 6

Machine learning approach to classifying declines of physical function and muscle strength associated with cognitive function in older women: gait characteristics based on three speeds

Kim, B., Youm, C., Park, H., Choi, H., & Shin, S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1376736 · Cited by 4

Untitled work

(2014) · Data Archiving and Networked Services (DANS)