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Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study

Bellini, G., Cipriano, M., Comai, S., Angeli, N. D., Gargano, J. P., Gianella, M., Goi, G., Ingrao, G., Masciadri, A., Rossi, G., et al. (2021) · Sensors · doi:10.3390/s21062147 · Cited by 16

Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes

Gao, Y., Gu, L., Wang, Y., Yang, F., & Yang, F. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0759-2 · Cited by 16

Spatial Transferability of Neural Network Models in Travel Demand Modeling

Tang, L., Xiong, C., & Zhang, L. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000752 · Cited by 15

InstaIndoor and multi-modal deep learning for indoor scene recognition

Glavan, A. I., & Talavera, E. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06781-2 · Cited by 15

Lessons in neural network training: overfitting may be harder than expected

Lawrence, S., Giles, C. L., & Tsoi, A. C. (1997) · Cited by 242

Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping

Caruana, R., Lawrence, S., & Giles, C. L. (2000) · Cited by 906

A study of the generalization capability versus training in backpropagation neural networks

Dalianis, P. J., Tzafestaş, S. G., & Anthopoulos, G. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1993.390760 · Cited by 10

An analysis of underfitting in MLP networks

Narayan, S., & Tagliarini, G. A. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1555986 · Cited by 20

Comparison of Backpropagation and Conjugate Gradient Algorithms for Efficient Training of Multilayer Perceptron Networks

Miao, G. (2002) · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University)