Overfitting and neural networks: conjugate gradient and backpropagation

Advances in neural information processing systems 7

(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759

Neural Networks and the Bias/Variance Dilemma

Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578

Untitled work

(1989) · Cited by 3,214

Advances in Neural Information Processing Systems 37

Touretzky, D. S. (2024) · doi:10.52202/079017 · Cited by 2,874

Advances in Neural Information Processing Systems 5

Zemel, R. S., Williams, C. K. I., & Mozer, M. C. (1993) · Cited by 1,896

What Size Net Gives Valid Generalization?

Baum, E. B., & Haussler, D. H. (1989) · Neural Computation · doi:10.1162/neco.1989.1.1.151 · Cited by 1,549

Untitled work

(1992) · Cited by 1,405

The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems

Moody, J. E. (1991) · Cited by 437

For Valid Generalization the Size of the Weights is More Important than the Size of the Network

Bartlett, P. L. (1996) · Cited by 213

Note on Learning Rate Schedules for Stochastic Optimization

Darken, C. J., & Moody, J. E. (1990) · neural information processing systems · Cited by 133

Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

Lu, H., Huang, C.-Y., Su, C., & Lin, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094917 · Cited by 15

Spiking neural networks for financial data prediction

Reid, D. C., Hussain, A. J., & Tawfik, H. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6707140 · Cited by 15

Master and Rookie Networks for Person Re-identification

Avola, D., Cascio, M., Cinque, L., Fagioli, A., Foresti, G. L., & Massaroni, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29891-3_41 · Cited by 13

Deep Learning For Fault Detection In Transformers Using Vibration Data

Rucconi, V., Maria, L. D., Garatti, S., Bartalesi, D., Valecillos, B., & Bittanti, S. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.369 · Cited by 15

A new pruning algorithm for neural network dimension analysis

Sabo, D., & Yu, X.-H. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634268 · Cited by 15

Evolutionary algorithm for training compact single hidden layer feedforward neural networks

Huynh, H. T., & Won, Y. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634225 · Cited by 15

POLAR: Attention-Based CNN for One-Shot Personalized Article Recommendation

Du, Z., Tang, J., & Ding, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10928-8_40 · Cited by 16

Ground-level ozone prediction using multilayer perceptron trained with an innovative hybrid approach

Wang, D., & Lu, J. W. Z. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.05.031 · Cited by 15

Gaussian Filters and Filter Synthesis Using a Hermite/Laguerre Neural Network

Mackenzie, M. R., & Tieu, A. K. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820558 · Cited by 15

3D texture-based face recognition system using fine-tuned deep residual networks

Zheng, S., Rahmat, R. W. O. K., Khalid, F., & Nasharuddin, N. A. (2019) · PeerJ Computer Science · doi:10.7717/peerj-cs.236 · Cited by 14

A concise peephole model based transfer learning method for small sample temporal feature-based data-driven quality analysis

Luo, W., Zhang, J., Feng, P., Yu, D., & Wu, Z. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105665 · Cited by 14

Convolutional Neural Networks for Inference of Space Object Attitude Status

Badura, G. P., Valenta, C. R., & Gunter, B. C. (2022) · The Journal of the Astronautical Sciences · doi:10.1007/s40295-022-00309-z · Cited by 15

Training Convolutional Neural Networks with Class Based Data Augmentation for Detecting Distracted Drivers

Cronje, J., & Engelbrecht, A. P. (2017) · International Conference on Computer and Automation Engineering · doi:10.1145/3057039.3057070 · Cited by 14

Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks

Rubio, V. G., Rodrigo, J. A., Menéndez, J. M., Almodóvar, N. S., Mayordomo, J. M. L., & Álvarez, F. (2019) · Sensors · doi:10.3390/s19204484 · Cited by 14

Fractional Dynamics Identification via Intelligent Unpacking of the Sample Autocovariance Function by Neural Networks

Szarek, D., Sikora, G., Balcerek, M., Jabłoński, I., & Wyłomańska, A. (2020) · Entropy · doi:10.3390/e22111322 · Cited by 12

On optimal degree selection for polynomial kernel with support vector machines: Theoretical and empirical investigations

Ali, S., & Smith‐Miles, K. (2007) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.3233/kes-2007-11101 · Cited by 15

On affine state-space neural networks for system identification: Global stability conditions and complexity management

Gil, P. S., Henriques, J. M. O., Cardoso, A., & Dourado, A. (2012) · Control Engineering Practice · doi:10.1016/j.conengprac.2012.11.008 · Cited by 13

Mathematical Methods for Knowledge Discovery and Data Mining

Felici, G., & Vercellis, C. (2008) · IGI Global eBooks · doi:10.4018/978-1-59904-528-3 · Cited by 13

VideoTrain: A Generative Adversarial Framework for Synthetic Video Traffic Generation

Madarasingha, C., Muramudalige, S. R., Choi, K. N., Jourjon, G., Wang, H., Jayasumana, A. P., & Thilakarathna, K. (2021) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom51794.2021.00034 · Cited by 12

Semantic rule-based information extraction for meteorological reports

Cui, M., Huang, R., Hu, Z., Xia, F., Xu, X., & Qi, L. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01885-8 · Cited by 12

Unifying topological structure and self-attention mechanism for node classification in directed networks

Peng, Y., Xia, J., Liu, D., Liu, M., Xiao, L., & Shi, B. (2025) · Scientific Reports · doi:10.1038/s41598-024-84816-z · Cited by 13

Which risk factor best predicts coronary artery disease using artificial neural network method?

Azdaki, N., Salmani, F., Kazemi, T., Partovi, N., Bizhaem, S. K., Moghadam, M. N., Moniri, Y., Zarepur, E., Mohammadifard, N., Alikhasi, H., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02442-1 · Cited by 12

Forecasting stock index with multi-objective optimization model based on optimized neural network architecture avoiding overfitting

Zhou, T., Hou, M., & Chunhui, L. (2017) · Computer Science and Information Systems · doi:10.2298/csis170125042t · Cited by 12

t-SNE based feature extraction technique for multi-layer perceptron neural network classifier

Mukherjee, S. (2017) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict1.2017.8342641 · Cited by 12

Classifying sensitive content in online advertisements with deep learning

Austin, D., Sanzgiri, A., Sankaran, K., Ryan Woodard, A., Lissack, A., & Seljan, S. S. (2020) · International Journal of Data Science and Analytics · doi:10.1007/s41060-020-00212-6 · Cited by 12

Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator

Mondal, S., Torelli, R., Lusch, B., Milan, P. J., & Magnotti, G. M. (2021) · SAE International Journal of Advances and Current Practices in Mobility · doi:10.4271/2021-01-0550 · Cited by 12

A liquid handling system for the automated acquisition of data for training, validating and testing calibration models

Richards, E. C. M., Bessant, C., & Saini, S. (2002) · Sensors and Actuators B Chemical · doi:10.1016/s0925-4005(02)00318-0 · Cited by 12

Application of artificial neural networks and Langmuir and Freundlich isotherm models to the removal of textile dye using biosorbents: A comparative study among methodologies

Petroli, G., de Leon, V. B., Domenico, M. D., de Souza, F. B., & Brusamarello, C. Z. (2024) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25271 · Cited by 11

Deep learning accurately predicts white shark locomotor activity from depth data

Liu, Z. Y.-C., Moxley, J. H., Kanive, P. E., Gleiss, A. C., Maughan, T., Bird, L. E., Jewell, O. J. D., Chapple, T. K., Gagné, T. O., White, C. F., et al. (2019) · Animal Biotelemetry · doi:10.1186/s40317-019-0175-5 · Cited by 12

Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma

Fan, S., Zhao, Z., Zhang, Y., Yu, H., Zheng, C., Huang, X., Yang, Z., Xing, M., Lu, Q. L., & Luo, Y. (2021) · BioData Mining · doi:10.1186/s13040-021-00272-9 · Cited by 11

State-Space Kriging: A Data-Driven Method to Forecast Nonlinear Dynamical Systems

Carnerero, A. D., Ramı́rez, D. R., & Álamo, T. (2022) · IEEE Control Systems Letters · doi:10.1109/lcsys.2021.3140167 · Cited by 11

Network Complexity Analysis of Multilayer Feedforward Artificial Neural Networks

Yu, H. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-10690-3_3 · Cited by 11

The Application of the Neural Network Model Inspired by the Immune System in Financial Time Series Prediction

Mahdi, A., Hussain, A. J., Lisbo, P., & Al‐Jumeily, D. (2009) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2009.39 · Cited by 11

Adaptive Neural Network Model Using the Immune System for Financial Time Series Forecasting

Mahdi, A., Hussain, A. J., & Al‐Jumeily, D. (2009) · International Conference on Computational Intelligence, Modelling and Simulation · doi:10.1109/cssim.2009.57 · Cited by 11

Using a neural network to predict deviations in mean heart dose during the treatment of left-sided deep inspiration breath hold patients

Malone, C., Fennell, L., Folliard, T., & Kelly, C. (2019) · Physica Medica · doi:10.1016/j.ejmp.2019.08.014 · Cited by 11

Tele-EvalNet: A Low-Cost, Teleconsultation System for Home Based Rehabilitation of Stroke Survivors Using Multiscale CNN-ConvLSTM Architecture

Kanade, A., Sharma, M., & Manivannan, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_50 · Cited by 10

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients

Yang, M., Liu, S., Hao, T., Ma, C., Chen, H., Li, Y., Wu, C., Xie, J., Qiu, H., Li, J., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102785 · Cited by 10

SLT-Based ELM for Big Social Data Analysis

Oneto, L., Bisio, F., Cambria, E., & Anguita, D. (2016) · Cognitive Computation · doi:10.1007/s12559-016-9440-6 · Cited by 10

Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances

Wahid, N. A., Bae, H., Adi, T. N., Choi, Y.-L., & Iskandar, Y. A. (2021) · Applied Sciences · doi:10.3390/app11219848 · Cited by 10

Assessment of Ankle Fractures using Deep Learning Algorithms and Convolutional Neural Network

Ashkani‐Esfahani, S., Mojahed-Yazdi, R., Bhimani, R., Kerkhoffs, G. M. M. J., Maas, M. M., Guss, D., DiGiovanni, C. W., & Lubberts, B. (2022) · Foot & Ankle Orthopaedics · doi:10.1177/2473011421s00091 · Cited by 10

Reduced-order hybrid modelling for powder compaction: Predicting density and classifying diametrical hardness

Trower, M., Emerson, J., Yu, M., Vivacqua, V., Johnson, T. C., Stitt, E. H., & dos Reis, G. (2023) · Powder Technology · doi:10.1016/j.powtec.2023.118745 · Cited by 10

Analysis of Accuracy and Epoch on Back-propagation BFGS Quasi-Newton

Silaban, H. F., Zarlis, M., & Sawaluddin, S. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/930/1/012006 · Cited by 10

Hierarchical segment-channel attention network for explainable multichannel signal classification

Lee, J., Do, H., Kwak, M. G., Kahng, H., & Kim, S. B. (2021) · Information Sciences · doi:10.1016/j.ins.2021.03.024 · Cited by 10

Weighted Least Squares Scheme for Reducing Effects of Outliers in Regression based on Extreme Learning Machine.

Huynh, H. T., & Won, Y. (2008) · Cited by 10

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

Seok, W., Yeo, M., You, J., Lee, H. J., Cho, T., Hwang, B., & Park, C. (2020) · Electronics · doi:10.3390/electronics9010142 · Cited by 10

DNA Microarray Classification with Compact Single Hidden-Layer FeedForward Neural Networks

Huynh, H. T., Kim, J., & Won, Y. (2007) · Frontiers in Convergence of Bioscience and Information Technologies · doi:10.1109/fbit.2007.30 · Cited by 10

Segmentation-based quantification of Tuta absoluta’s damage on tomato plants

Loyani, L. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100415 · Cited by 11

A framework of evolutionary optimized convolutional neural network for classification of shang and chow dynasties bronze decorative patterns

Qi, X., He, X., Chen, S. W., & Hai, T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0293517 · Cited by 9

Intelligent Fault Detection Scheme for Rolling Bearing Based on Generative Adversarial Network and AutoEncoders Using Convolutional Neural Network

Rathore, M. S., & Harsha, S. P. (2024) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-024-01580-0 · Cited by 9

A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease

Scodellaro, R., Zschüntzsch, J., Hell, A. K., & Alves, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108733 · Cited by 11

NU-AIR: A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles

Iaboni, C., Kelly, T., & Abichandani, P. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02418-2 · Cited by 10

Class-weighted reinforcement learning for skin cancer image classification

Mayanja, A., Doğan, N., & Taşdemir, Ş. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128426 · Cited by 9

ConvMOS: climate model output statistics with deep learning

Steininger, M., Abel, D., Ziegler, K., Krause, A., Paeth, H., & Andreas Hotho, A. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00877-6 · Cited by 10

Short-Term Streamflow Forecasting for Paraíba do Sul River Using Deep Learning

Campos, L. C. D., Goliatt, L., Fonseca, T. L., de Abreu, G. D., Pires, L. F., & Gorodetskaya, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30241-2_43 · Cited by 9

Multiclass semantic segmentation for digitisation of movable heritage using deep learning techniques

Patrucco, G., & Setragno, F. (2021) · Virtual Archaeology Review · doi:10.4995/var.2021.15329 · Cited by 9

Machine-learning-based quality-level-estimation system for inspecting steel microstructures

Nishiura, H., Miyamoto, A., Ito, A., Harada, M., Suzuki, S., Fujii, K., Morifuji, H., & Takatsuka, H. (2022) · Microscopy · doi:10.1093/jmicro/dfac019 · Cited by 9

Projection pursuit Gaussian process regression

Chen, G., & Tuo, R. (2022) · IISE Transactions · doi:10.1080/24725854.2022.2121882 · Cited by 11

Dual-View Preference Learning for Adaptive Recommendation

Liu, Z., Fang, Y., & Wu, M. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3236370 · Cited by 6

Lazy Overfitting Control

Prieditis, A. E., & Sapp, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39712-7_37 · Cited by 9

A Neural Network Approach to Understanding Implied Volatility MovementsPreprint

Cao, J., Chen, J. H. K., & Hull, J. C. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3288067 · Cited by 10

X-in-the-Loop Validation of Deep Learning-Based Virtual Sensing for Lifetime Estimation of Automotive Power Electronics Converters

Chakraborty, S., Bhoi, S. K., Hosseinabadi, F., Davari, P., Blaabjerg, F., & Hegazy, O. (2024) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2024.3391930 · Cited by 12

Combining SDAE Network with Improved DTW Algorithm for Similarity Measure of Ultra-Weak FBG Vibration Responses in Underground Structures

Li, S., Zuo, X., Li, Z., Wang, H., & Sun, L. (2020) · Sensors · doi:10.3390/s20082179 · Cited by 8

Prediction of Paratope–Epitope Pairs Using Convolutional Neural Networks

Li, D., Pucci, F., & Rooman, M. J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25105434 · Cited by 11

In Depth Analysis, Applications and Future Issues of Artificial Neural Network

Soni, B., Mathur, P., & Bora, A. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-52067-0_7 · Cited by 9

Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

Morfidis‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌, K., & Kostinakis, K. (2022) · Applied Sciences · doi:10.3390/app12031055 · Cited by 8

Improving lab-of-origin prediction of genetically engineered plasmids via deep metric learning

Soares, I. M., Camargo, F. H. F., Marques, A., & Crook, O. M. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00234-z · Cited by 9

Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats

Jadhav, K. S., Jamot, B. B., Deroche‐Gamonet, V., Belin, D., & Boutrel, B. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15839 · Cited by 7

Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics

Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., Mazzino, N., & Anguita, D. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47898-2_15 · Cited by 8

Heterogeneous domain adaptation with label and structural consistency

Huang, J., Zhou, Z., Shang, J., & Niu, C. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08731-x · Cited by 8

On State-Space Neural Networks for Systems Identification: Stability and Complexity

Gil, P. S., Henriques, J. M. O., Dourado, A., & Duarte-Ramos, H. (2006) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2006.252333 · Cited by 8

Indoor Location and Collision Feedback for a Powered Wheelchair System Using Machine Learning

Bausch, N., Shilling, P., Sanders, D. A., Haddad, M., Okonor, O. M., & Tewkesbury, G. E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29516-5_54 · Cited by 7

Bayesian Neural Network-Based Ground Motion Model for Horizontal and V/H Spectral Ordinate with Epistemic Uncertainty for the European Region

Yedulla, J., Sriwastav, R. K., Choubey, A., Tulasi, K. S., & Raghukanth, S. T. G. (2025) · Journal of Earthquake Engineering · doi:10.1080/13632469.2025.2528913 · Cited by 7

Alzheimer’s disease detection from magnetic resonance imaging: a deep learning perspective

Armonaité, K., Ventura, M. L., & Laura, L. (2023) · Exploration of Neuroprotective Therapy · doi:10.37349/ent.2023.00043 · Cited by 8

From optimal observables to machine learning: an effective-field-theory analysis of e^+e^− → W^+W^− at future lepton colliders

Chai, S.-D., Gu, J.-Y., & Li, L.-F. (2024) · Journal of High Energy Physics · doi:10.1007/jhep05(2024)292 · Cited by 8

Image mapping the temporal evolution of edge characteristics in tokamaks using neural networks

Gopakumar, V., & Samaddar, D. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab5639 · Cited by 7

Application of machine learning techniques to predict rupture propagation and arrest in 2-D dynamic earthquake simulations

Ahamed, S., & Daub, E. G. (2020) · Geophysical Journal International · doi:10.1093/gji/ggaa547 · Cited by 14

Generation of Synthetic Elevation Models and Realistic Surface Images of River Deltas and Coastal Terrains Using cGANs

Valencia-Rosado, L. O., Guzman-Zavaleta, Z. J., & Starostenko, O. (2020) · IEEE Access · doi:10.1109/access.2020.3048083 · Cited by 8

Price Forecast for Mexican Red Spiny Lobster (Panulirus spp.) Using Artificial Neural Networks (ANNs)

Hernández-Casas, S., Beltrán-Morales, L. F., Vargas-López, V. G., Vergara‐Solana, F. J., & Seijo, J. C. (2022) · Applied Sciences · doi:10.3390/app12126044 · Cited by 7

Prediction of bradycardia in preterm infants using artificial neural networks

Jiang, H., Salmon, B. P., Gale, T. J., & Dargaville, P. A. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100426 · Cited by 7

Dynamic Facial Stress Recognition in Temporal Convolutional Network

Feng, S. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36808-1_76 · Cited by 8

Recovery of linear components: Reduced complexity autoencoder designs

Zocco, F., & McLoone, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104663 · Cited by 8

Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition

Ali, W., Kumar, J., Xu, Z., Kumar, R., & Ren, Y. (2021) · Applied Sciences · doi:10.3390/app11199038 · Cited by 6

Data-level transfer learning for degradation modeling and prognosis

Fallahdizcheh, A., & Wang, C. (2022) · Journal of Quality Technology · doi:10.1080/00224065.2022.2081103 · Cited by 7

A study on a low power optimization algorithm for an edge-AI device

Kaneko, T., Orimo, K., Hida, I., Takamaeda-Yamazaki, S., Ikebe, M., Motomura, M., & Asai, T. (2019) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.10.373 · Cited by 8

Exploring function estimators as an alternative to regression in psychology

Walker, I. O., & Milne, S. (2005) · Behavior Research Methods · doi:10.3758/bf03206395 · Cited by 7

High-Throughput Clinical Trial Emulation with Real World Data and Machine Learning: A Case Study of Drug Repurposing for Alzheimer’s DiseasePreprint

Zang, C., Zhang, H., Xu, J., Zhang, H., Fouladvand, S., Havaldar, S., Cheng, F., Chen, K., Chen, Y., Glicksberg, B. S., et al. (2022) · medRxiv · doi:10.1101/2022.01.31.22270132 · Cited by 7

Comparative analysis of deep learning architectures for thyroid eye disease detection using facial photographs

Aghajani, A., Rajabi, M. T., Rafizadeh, S. M., Zand, A., Rezaei, M., Shojaeinia, M., & Rahmanikhah, E. (2025) · BMC Ophthalmology · doi:10.1186/s12886-025-03988-y · Cited by 6

Extracting and Analyzing Cybersecurity Named Entity and its Relationship with Noncontextual IOCs from Unstructured Text of CTI Sources

Fujii, S., Kawaguchi, N., Shigemoto, T., & Yamauchi, T. (2023) · Journal of Information Processing · doi:10.2197/ipsjjip.31.578 · Cited by 6

Inter-Satellite Link Prediction with Supervised Learning Based on Kepler and SGP4 Orbits

Ferrer, E., Ruiz-de-Azua, J. A., Betorz, F., & Escrig, J. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00610-9 · Cited by 6

Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data

Ata, N., Zahoor, I., Hoda, N., Adnan, S. M., Vijayakumar, S., Louis, F., Poisson, L., Rattan, R., Kumar, N., Cerghet, M., et al. (2024) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2024.105942 · Cited by 7

Topology-adaptive Bayesian optimization for deep ring echo state networks in speech emotion recognition

Soltani, R., Benmohamed, E., & Ltifi, H. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10519-1 · Cited by 6

Ridge Polynomial Neural Network for Non-destructive Eddy Current Evaluation

Hacib, T., Bihan, Y. L., Mekideche, M. R., & Ferkha, N. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-16225-1_10 · Cited by 6

Distance-based discriminant analysis method and its applications

Kosinov, S., & Pun, T. (2007) · Pattern Analysis and Applications · doi:10.1007/s10044-007-0082-x · Cited by 6

Neural network dimension selection for dynamical system identification

Sabo, D., & Yu, X.-H. (2008) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2008.4629704 · Cited by 6

Hierarchical Attention Network with XGBoost for Recognizing Insufficiently Supported Argument

Suhartono, D., Gema, A. P., Winton, S., David, T., Fanany, M. I., & Arymurthy, A. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69456-6_15 · Cited by 6

Why Mixup Improves the Model Performance

Kimura, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86340-1_22 · Cited by 6

Data augmentation-empowered diabetic retinopathy detection based on collaborative discrimination-enabled generative adversarial network

Zhang, Z., Li, Y., Liu, T., & Liu, C. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06147-6 · Cited by 5

Responsible guidelines for authorship attribution tasks in NLP

Saxena, V., Tamò‐Larrieux, A., DIJCK, G. V., & Spanakis, G. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09821-w · Cited by 7

Genetic Algorithm based Resource Broker for Computational Grid

Singh, S., Sarkar, M., Roy, S., & Mukherjee, N. (2013) · Procedia Technology · doi:10.1016/j.protcy.2013.12.397 · Cited by 5

Self-organized Neural Network Inspired by the Immune Algorithm for the Prediction of Speech Signals

Al‐Jumeily, D., Hussain, A. J., Fergus, P., & Radi, N. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22186-1_65 · Cited by 5

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)