Neural Networks and the Bias/Variance Dilemma

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

Multilayer feedforward networks are universal approximators

Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030

Information Theory and an Extension of the Maximum Likelihood Principle

Akaike, H. (1998) · Springer series in statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 18,636

Learning Internal Representations by Error Propagation

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501

Approximation by superpositions of a sigmoidal function

Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883

Untitled work

(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines

Demertzis, K., Tsiotas, D., & Magafas, L. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17134693 · Cited by 63

Machine Learning Algorithms for Predicting and Risk Profiling of Cardiac Surgery-Associated Acute Kidney Injury

Penny‐Dimri, J. C., Bergmeir, C., Reid, C. M., Williams‐Spence, J., Cochrane, A. D., & Smith, J. A. (2020) · Seminars in Thoracic and Cardiovascular Surgery · doi:10.1053/j.semtcvs.2020.09.028 · Cited by 56

Feature Extraction for One-Class Classification

Tax, D. M. J., & Müller, K.-R. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44989-2_41 · Cited by 56

Causality: Objectives and assessment

Guyon, I., Janzing, D., & Schölkopf, B. (2008) · Cited by 56

Reservoir Properties from Well Logs using neural Networks

Bhatt, A. (2002) · Cited by 57

Trade-Off Between Diversity and Accuracy in Ensemble Generation

Chandra, A., Chen, H., & Yao, X. (2006) · Springer eBooks · doi:10.1007/11399346_19 · Cited by 57

Knowledge selection in category learning

Heit, E., & Bott, L. A. (2000) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(00)80034-1 · Cited by 56

Bayesian Inference for the Spatio-Temporal Invasion of Alien Species

Cook, A. R., Marion, G., Butler, A., & Gibson, G. J. (2007) · Bulletin of Mathematical Biology · doi:10.1007/s11538-007-9202-4 · Cited by 55

Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming

Vajihi, Z., Rosado-Méndez, I. M., Hall, T. J., & Rivaz, H. J. (2018) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2018.2869810 · Cited by 55

A cognitive brain–computer interface monitoring sustained attentional variations during a continuous task

Gaume, A., Dreyfus, G., & Vialatte, F. (2019) · Cognitive Neurodynamics · doi:10.1007/s11571-019-09521-4 · Cited by 58

Linear and Order Statistics Combiners for Pattern Classification

SHARKEY, A. J. C. (1999) · Perspectives in neural computing · doi:10.1007/978-1-4471-0793-4_6 · Cited by 56

The new science of simplicity

Forster, M. R. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511493164.005 · Cited by 54

Factored Particles for Scalable MonitoringPreprint

Ng, B., Peshkin, L., & Pfeffer, A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.0590 · Cited by 55

Beyond Playing 20 Questions with Nature: Integrative Experiment Design in the Social and Behavioral SciencesPreprint

Almaatouq, A., Griffiths, T. L., Suchow, J. W., Whiting, M. E., Evans, J. A., & Watts, D. J. (2022) · doi:10.31234/osf.io/anjkm · Cited by 57

Data augmentation: an alternative approach to the analysis of spectroscopic data

Conlin, A., Martin, E. B., & Morris, A. J. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00071-9 · Cited by 54

Quantifying uncertainties in the estimation of safety parameters by using bootstrapped artificial neural networks

Secchi, P., Zio, E., & Maio, F. D. (2008) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2008.07.010 · Cited by 54

Taking uncertainty seriously: simplicity versus complexity in financial regulation

Aikman, D., Galešić, M., Gigerenzer, G., Kapadia, S., Katsikopoulos, K. V., Kothiyal, A., Murphy, E., & Neumann, T. (2020) · Industrial and Corporate Change · doi:10.1093/icc/dtaa024 · Cited by 57

A Bias-Variance-Complexity Trade-Off Framework for Complex System Modeling

Yu, L., Lai, K. K., Wang, S., & Huang, W. (2006) · Lecture notes in computer science · doi:10.1007/11751540_55 · Cited by 54

Adaptive Regularization in Neural Network Modeling

Larsen, J. O., Svarer, C., Andersen, L. N., & Hansen, L. K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_8 · Cited by 54

On the Dimensionality of Face Space

Meytlis, M., & Sirovich, L. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1033 · Cited by 54

Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Massaoudi, M., Chihi, I., Sidhom, L., Trabelsi, M., Refaat, S. S., & Oueslati, F. S. (2021) · Energies · doi:10.3390/en14133992 · Cited by 56

Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts

Baskozos, G., Themistocleous, A. C., Hébert, H. L., Pascal, M. M. V., John, J., Callaghan, B. C., Laycock, H. C., Granovsky, Y., Crombez, G., Yarnitsky, D., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01890-x · Cited by 57

Enhancing One-Day-Ahead Probabilistic Solar Power Forecast With a Hybrid Transformer-LUBE Model and Missing Data Imputation

Phan, Q., Wu, Y., & Phan, Q. D. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3325798 · Cited by 54

Computer‐Aided Prediction of Rodent Carcinogenicity by PASS and CISOC‐PSCT

Lagunin, A. A., Filimonov, D. A., Zakharov, A. V., Xie, W., Huang, Y., Zhu, F., Shen, T., Yao, J., & Poroikov, V. (2009) · QSAR & Combinatorial Science · doi:10.1002/qsar.200860192 · Cited by 53

Statistical Feature Recognition for Multidimensional Solar Imagery

Turmon, M., Jones, H. P., Malanushenko, O., & Pap, J. M. (2010) · Solar Physics · doi:10.1007/s11207-009-9490-y · Cited by 53

Logarithmic opinion pools for conditional random fields

Smith, A., Cohn, T., & Osborne, M. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219843 · Cited by 53

Multiple model combination methods for annual maximum water level prediction during river ice breakup

Sun, W., & Trevor, B. (2017) · Hydrological Processes · doi:10.1002/hyp.11429 · Cited by 52

Knowledge discovery by a neuro-fuzzy modeling framework

Castellano, G., Castiello, C., Fanelli, A. M., & Mencar, C. (2004) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2004.07.015 · Cited by 53

Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network

Dudul, S. V. (2004) · Applied Soft Computing · doi:10.1016/j.asoc.2004.07.005 · Cited by 53

Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms

Hasselmann, K., Ligot, A., Ruddick, J., & Birattari, M. (2021) · Nature Communications · doi:10.1038/s41467-021-24642-3 · Cited by 53

A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction

Jeong, C., Min, J. H., & Kim, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.056 · Cited by 52

Ensemble of neural networks with associative memory (ENNA) for estimating software development costs

Kultur, Y., Turhan, B., & Bener, A. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.05.001 · Cited by 52

Heteroscedastic kernel ridge regression

Cawley, G. C., Talbot, N. L. C., Foxall, R., Dorling, S. R., & Mandic, D. P. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.01.005 · Cited by 52

Forecasting the NN5 time series with hybrid models

Wichard, J. (2010) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2010.02.011 · Cited by 53

System-level training of neural networks for counting white blood cells

Theera‐Umpon, N., & Gader, P. (2002) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2002.1009139 · Cited by 52

Colour Constancy Beyond the Classical Receptive Field

Akbarinia, A., & Párraga, C. A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2753239 · Cited by 52

The Bias-Variance Tradeoff: How Data Science Can Inform Educational Debates

Doroudi, S. (2020) · AERA Open · doi:10.1177/2332858420977208 · Cited by 59

Information theoretic subset selection for neural network models

Sridhar, D. V., Bartlett, E. B., & Seagrave, R. C. (1998) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(97)00227-5 · Cited by 52

Data Mining and Visualization

Kohavi, R. (2000) · Cited by 52

Multi-layer Perceptron Error Surfaces: Visualization, Structure and Modelling

Gallagher, M. R. (2000) · The University of Queensland · doi:10.14264/157842 · Cited by 51

A Smoothing Regularizer for Feedforward and Recurrent Neural Networks

Wu, L., & Moody, J. E. (1996) · Neural Computation · doi:10.1162/neco.1996.8.3.461 · Cited by 51

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

Meleiro, L. A. D. C., Zuben, F. J. V., & Filho, R. M. (2008) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2008.06.001 · Cited by 51

When should we use simple decision models? A synthesis of various research strands

Katsikopoulos, K. V., Durbach, I. N., & Stewart, T. J. (2017) · Omega · doi:10.1016/j.omega.2017.09.005 · Cited by 56

Heuristics as Bayesian inference under extreme priors

Parpart, P., Jones, M., & Love, B. C. (2018) · Cognitive Psychology · doi:10.1016/j.cogpsych.2017.11.006 · Cited by 58

An integrated artificial neural network and fuzzy clustering algorithm for performance assessment of decision making units

Azadeh, A., Ghaderi, S. F., Anvari, M., Saberi, M., & Izadbakhsh, H. (2006) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.08.092 · Cited by 51

Flexible Non-linear Approaches to Classification

Ripley, B. D. (1994) · Springer eBooks · doi:10.1007/978-3-642-79119-2_5 · Cited by 51

Resource-Efficient Convolutional Networks: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques

Lee, J., Mukhanov, L., Molahosseini, A. S., Minhas, U. I., Yang, H., del Rincón, J. M., Dichev, K., Hong, C.-H., & Vandierendonck, H. (2023) · ACM Computing Surveys · doi:10.1145/3587095 · Cited by 63

Determining the optimal number of folds to use in a K-fold cross-validation: A neural network classification experiment

Oyedele, O. (2023) · Research in Mathematics · doi:10.1080/27684830.2023.2201015 · Cited by 50

Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters

Zhang, T., Fell, F., Liu, Z., Preusker, R., Fischer, J., & He, M. (2003) · Journal of Geophysical Research Atmospheres · doi:10.1029/2002jc001638 · Cited by 50

Threefold vs. fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural network modeling of machining surface roughness data

Feng, C.-X. J., Yu, Z.-G. S., Kingi, U., & Baig, M. P. (2005) · Journal of Manufacturing Systems · doi:10.1016/s0278-6125(05)80010-x · Cited by 50

Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches

Wu, N., Huang, J., Schmalz, B., & Fohrer, N. (2013) · Limnology · doi:10.1007/s10201-013-0412-1 · Cited by 50

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

Lazri, M., & Ameur, S. (2017) · Atmospheric Research · doi:10.1016/j.atmosres.2017.12.006 · Cited by 54

How to Shift Bias: Lessons from the Baldwin Effect

Turney, P. D. (1996) · Evolutionary Computation · doi:10.1162/evco.1996.4.3.271 · Cited by 50

Reduced perceptual dimensionality in extrafoveal vision

Jüttner, M., & Rentschler, I. (1996) · Vision Research · doi:10.1016/0042-6989(95)00250-2 · Cited by 50

On Global–Local Artificial Neural Networks for Function Approximation

Wedge, D. C., Ingram, D. M., McLean, D., Mingham, C. G., & Bandar, Z. A. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875972 · Cited by 50

An Introduction to Model Selection: Tools and Algorithms

Hélie, S. (2006) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.02.1.p001 · Cited by 50

Models of dataset size, question design, and cross-language speech perception for speech crowdsourcing applications

Hasegawa‐Johnson, M., Cole, J., Jyothi, P., & Varshney, L. R. (2015) · Laboratory Phonology Journal of the Association for Laboratory Phonology · doi:10.1515/lp-2015-0012 · Cited by 48

The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining

Pham, H. N. A., & Triantaphyllou, E. (2007) · Springer eBooks · doi:10.1007/978-0-387-69935-6_16 · Cited by 47

Optimization of the generalization capability for rainfall–runoff modeling by neural networks: the case of the Lez aquifer (southern France)

Siou, L. K. A., Johannet, A., Estupina, V. B., & Pistre, S. (2011) · Environmental Earth Sciences · doi:10.1007/s12665-011-1450-9 · Cited by 49

SCORER 2.0: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences

Armstrong, C. T., Vincent, T. L., Green, P. J., & Woolfson, D. N. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr299 · Cited by 49

Nonlinear multitrace genetic inversion applied on seismic data across the Shtokman field, offshore northern Russia

Veeken, P. C. H., Приезжев, И. И., Shmaryan, L. E., Штейн, Я. И., Barkov, A., & Ampilov, Y. P. (2009) · Geophysics · doi:10.1190/1.3223314 · Cited by 49

Classification study of skin sensitizers based on support vector machine and linear discriminant analysis

Ren, Y., Liu, H., Xue, C., Yao, X., Liu, M., & Fan, B. (2006) · Analytica Chimica Acta · doi:10.1016/j.aca.2006.05.027 · Cited by 49

Architecture for development of adaptive on-line prediction models

Kadlec, P., & Gabryś, B. (2009) · Memetic Computing · doi:10.1007/s12293-009-0017-8 · Cited by 49

An enhanced two-level Boolean synthesis methodology for fuzzy rules minimization

Rovatti, R., Guerrieri, R., & Baccarani, G. (1995) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.413227 · Cited by 49

Improved Class Probability Estimates from Decision Tree Models

Margineantu, D. D., & Dietterich, T. G. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_10 · Cited by 49

Lifelong Learning: A Case Study.

Thrun, S. (1995) · doi:10.21236/ada303191 · Cited by 49

Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river

Piotrowski, A., Osuch, M., Napiorkowski, M. J., Rowiński, P. M., & Napiorkowski, J. J. (2014) · Computers & Geosciences · doi:10.1016/j.cageo.2013.12.013 · Cited by 47

The "weight smoothing" regularization of MLP for Jacobian stabilization

Aires, F., Schmitt, M., Chédin, A., & Scott, N. A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.809096 · Cited by 49

Multi-objective ensemble forecasting with an application to power transformers

Peimankar, A., Weddell, S. J., Jalal, T. S., & Lapthorn, A. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.03.042 · Cited by 48

Building the Theory of Ecological Rationality

Todd, P. M., & Brighton, H. (2015) · Minds and Machines · doi:10.1007/s11023-015-9371-0 · Cited by 56

Simple modifications of the nonlinear regression stream temperature model for daily data

Piotrowski, A., & Napiorkowski, J. J. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.02.035 · Cited by 51

Motor Fault Diagnosis Using CNN Based Deep Learning Algorithm Considering Motor Rotating Speed

Han, J.-H., Choi, D.-J., Hong, S.-K., & Kim, H.-S. (2019) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iea.2019.8714900 · Cited by 48

Coupled deep learning coded aperture design for compressive image classification

Bacca, J., Galvis, L. V., & Argüello, H. (2019) · Optics Express · doi:10.1364/oe.381479 · Cited by 52

Revealing Consensus and Dissensus between Network Partitions

Peixoto, T. P. (2021) · Physical Review X · doi:10.1103/physrevx.11.021003 · Cited by 53

Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?

Katsikopoulos, K. V., Şimşek, Ö., Buckmann, M., & Gigerenzer, G. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.12.006 · Cited by 51

On the role of psychological heuristics in operational research; and a demonstration in military stability operations

Keller, N., & Katsikopoulos, K. V. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.07.023 · Cited by 48

COMBINING REGRESSION TREES AND RADIAL BASIS FUNCTION NETWORKS

Orr, M. J. L., Hallam, J. C. T., Takezawa, K., Murray, A., Ninomiya, S., Oide, M., & Leonard, T. (2000) · International Journal of Neural Systems · doi:10.1016/s0129-0657(00)00036-3 · Cited by 48

Quantitative Analysis of Multivariate Data Using Artificial Neural Networks: A Tutorial Review and Applications to the Deconvolution of Pyrolysis Mass Spectra

Goodacre, R., Neal, M., & Kell, D. B. (1996) · Zentralblatt für Bakteriologie · doi:10.1016/s0934-8840(96)80004-1 · Cited by 48

A Framework for Combining Symbolic and Neural Learning

SHAVLIK, J. W. (1992) · Cited by 48

Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor PoliciesPreprint

(Sasha), A. S., Emi, B., Zamir, A., Guibas, L. J., Savarese, S., & Malik, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11971 · Cited by 70

Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring

Kong, J., Ryu, Y., Jeong, S., Zhong, Z., Choi, W., Kim, J., Lee, K.-D., Lim, J., Jang, K., Chun, J., et al. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.04.013 · Cited by 47

Being Aware of Data Leakage and Cross‐Validation Scaling in Chemometric Model Validation

Király, P., & Tóth, G. (2025) · Journal of Chemometrics · doi:10.1002/cem.70026 · Cited by 57

Clustering-based selective neural network ensemble

Fu, Q., Hu, S., & Shengying, Z. (2005) · 浙江大学学报：A卷英文版 · doi:10.1007/bf02839405 · Cited by 47

Neural Network Uncertainty Assessment Using Bayesian Statistics: A Remote Sensing Application

Aires, F., Prigent, C., & Rossow, W. B. (2004) · Neural Computation · doi:10.1162/0899766041941925 · Cited by 47

Structural diversity for decision tree ensemble learning

Sun, T., & Zhou, Z. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-7151-8 · Cited by 48

Photovoltaic Power Forecasting using LSTM on Limited Dataset

De, V., Teo, T. T., Woo, W. L., & Logenthiran, T. (2018) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2018.8467934 · Cited by 47

Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

Chen, M. S., Zuehlsdorff, T. J., Morawietz, T., Isborn, C. M., & Markland, T. E. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02168 · Cited by 50

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

Pham, T. H., Devalla, S. K., Ang, A. J. F., Soh, Z. D., Thiéry, A. H., Boote, C., Cheng, C.-Y., Girard, M. J. A., & Koh, V. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2019-315723 · Cited by 49

Unsupervised Domain Adaptation for Person Re-identification via Heterogeneous Graph Alignment

Zhang, M., Liu, K., Li, Y., Guo, S., Duan, H., Long, Y., & Jin, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16448 · Cited by 47

Curious machines: active learning with structured instances

Craven, M. W., & Settles, B. (2008) · Cited by 44

Modeling and Sensitivity Analysis of Acoustic Release of Doxorubicin from Unstabilized Pluronic P105 Using an Artificial Neural Network Model

Husseini, G. A., Jabbar, N. A., Mjalli, F. S., & Pitt, W. G. (2007) · Technology in Cancer Research & Treatment · doi:10.1177/153303460700600107 · Cited by 47

Using Taguchi's method of experimental design to control errors in layered perceptrons

Peterson, G. E., Clair, D. C. S., Aylward, S., & Bond, W. E. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.392257 · Cited by 47

Estimating structural exchange rate models by artificial neural networks

Plasmans, J. E. J., Verkooijen, W. J. H., & Daniels, H. A. M. (1998) · Applied Financial Economics · doi:10.1080/096031098332844 · Cited by 47

Cross-Validation Estimates IMSE

Plutowski, M., Sakata, S., & White, H. (1993) · neural information processing systems · Cited by 47

Analysis of error-reject trade-off in linearly combined multiple classifiers

Fumera, G., & Roli, F. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.12.005 · Cited by 46

Objective quality assessment of displayed images by using neural networks

Gastaldo, P., Zunino, R., Heynderickx, I. E. J., & Vicario, E. (2005) · Signal Processing Image Communication · doi:10.1016/j.image.2005.03.013 · Cited by 46

Enhanced incremental LMS with norm constraints for distributed in-network estimation

Liu, Y., & Tang, K. S. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.07.001 · Cited by 46

Novel genetic-based negative correlation learning for estimating soil temperature

Kazemi, S. M., Bidgoli, B. M., Shamshirband, S., Karimi, S. M., Ghorbani, M. A., Chau, K., & Pour, R. K. (2018) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2018.1463871 · Cited by 46

Automatic Generation of Flexible-Monomer Intermolecular Potential Energy Surfaces

Metz, M. P., & Szalewicz, K. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01241 · Cited by 46

Understanding the uneven spread of COVID-19 in the context of the global interconnected economy

Tsiotas, D., & Tselios, V. (2022) · Scientific Reports · doi:10.1038/s41598-021-04717-3 · Cited by 49

VIEW~~~~~~~~~~~~~~ ~ Communicated by Lawrence Jackel Neural Networks and the Bias/Variance Dilemma

Geman, S., Bienenstock, E., & Doursat, R. (2015)

Neural Networks and the BiadVariance Dilemma

Geman, S., & Bienenstock, E. (1992) · Cited by 14

VIEW Communicated by Lawrence Jackel Neural Networks and the BiadVariance Dilemma

Geman, S., Bienenstock, E., & Doursat, R. (2015)

Neural networks and nonparametric regression

Cherkassky, V. (2003) · Machine learning for signal processing ... · doi:10.1109/nnsp.1992.253661 · Cited by 8

Assessing generalization of feedforward neural networks

Turmon, M., & Fine, T. L. (2002) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.1995.531517 · Cited by 5