The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
Neural Networks and the Bias/Variance Dilemma
Abstract
Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.
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“The bias-variance trade-off implies that a model should balance under-fitting and over-fitting: rich enough to express underlying structure in data, simple enough to avoid fitting spurious patterns.”
From Reconciling modern machine-learning practice and the classical bias–variance trade-off (2019) · cites this paper as [17]
In the citing paper
“The concept of bias in machine learning (ML) research and more generally in the field of predictive modeling is intrinsically tied to the concept of variance.14”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [14]
Citations
Cites
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
(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
Flagged first, then by citations
Research works printed but not linked: 7
- 10.1016/S0364-0213(85)80012-4 · DOI · Paper page
- Barron A. R., 1989, Florida, 280
- Baum L. E., 1972, Inequalities, 3, 1
- Burr D. J., 1980, IEEE Transact. Pattern Anal. Machine Intell. PAMI-3, 6, 708
- Chauvin Y., 1990, Neural Information Processing Systems 11, D. S. Touretzky, ed., 642
- Denker J., 1987, Complex Syst., 1, 877
- Goldman L., 1982, J. Med., 307, 588
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Items 101–200 of 1,006
Manel, S., Dias, J.-M., & Ormerod, S. J. (1999) · Ecological Modelling · doi:10.1016/s0304-3800(99)00113-1 · Cited by 436
A Survey on Data Augmentation for Text Classification
Bayer, M., Kaufhold, M., & Reuter, C. (2022) · ACM Computing Surveys · doi:10.1145/3544558 · Cited by 496
Machine learning approaches for estimation of prediction interval for the model output
Shrestha, D. L., & SOLOMATINE, D. P. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.01.012 · Cited by 429
Sales forecasting using extreme learning machine with applications in fashion retailing
Sun, Z.-L., Choi, T., Au, K.-F., & Yu, Y. T. (2008) · Decision Support Systems · doi:10.1016/j.dss.2008.07.009 · Cited by 428
Thrun, S. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2_8 · Cited by 434
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.11.3 · Cited by 424
Binding in models of perception and brain function
von der Malsburg, C. (1995) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(95)80014-x · Cited by 414
Tetko, I. V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., Öberg, T., Todeschini, R., Fourches, D., & Varnek, A. A. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800151m · Cited by 430
Evaluation of gaussian processes and other methods for non-linear regression
Hinton, G. E., & Rasmussen, C. E. (1997) · Library and Archives Canada (Government of Canada) · Cited by 430
Language Evolution by Iterated Learning With Bayesian Agents
Griffiths, T. L., & Kalish, M. L. (2007) · Cognitive Science · doi:10.1080/15326900701326576 · Cited by 404
Key Concepts in Model Selection: Performance and Generalizability
Forster, M. R. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1999.1284 · Cited by 390
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Tokheim, C. J., Papadopoulos, N., Kinzler, K. W., Vogelstein, B., & Karchin, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616440113 · Cited by 378
Tetko, I. V., Tanchuk, V. Y., & Villa, A. E. P. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010368v · Cited by 382
QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction
Lagunin, A. A., Zakharov, A. V., Filimonov, D. A., & Poroikov, V. (2011) · Molecular Informatics · doi:10.1002/minf.201000151 · Cited by 389
Combining multiple classifiers by averaging or by multiplying?
Tax, D. M. J., van Breukelen, M., Duin, R. P. W., & Kittler, J. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00138-7 · Cited by 377
Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review
Soomro, T. A., Zheng, L., Afifi, A. J., Ali, A., Soomro, S., Yin, M., & Gao, J. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3185292 · Cited by 389
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019) · Academic Pathology · doi:10.1177/2374289519873088 · Cited by 381
Hyperparameter Search in Machine LearningPreprint
Claesen, M., & Moor, B. D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02127 · Cited by 496
Ince, R. A. A., Giordano, B. L., Kayser, C., Rousselet, G. A., Groß, J., & Schyns, P. G. (2016) · Human Brain Mapping · doi:10.1002/hbm.23471 · Cited by 363
Series in Machine Perception and Artificial Intelligence
(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 373
Memory neuron networks for identification and control of dynamical systems
SASTRY, P. S., Santharam, G., & Unnikrishnan, K. P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279193 · Cited by 362
Models for early prediction of at-risk students in a course using standards-based grading
Marbouti, F., Diefes‐Dux, H. A., & Madhavan, K. P. C. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.09.005 · Cited by 399
Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
Geman, D., d'Avignon, C., Naiman, D. Q., & Winslow, R. L. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1071 · Cited by 356
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
Blum, M. G. B., Nunes, M. A., Prangle, D., & Sisson, S. A. (2013) · Statistical Science · doi:10.1214/12-sts406 · Cited by 355
Ensemble deep learning in bioinformatics
Cao, Y., Geddes, T. A., Yang, J. Y. H., & Yang, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0217-y · Cited by 366
PCFG models of linguistic tree representations
Johnson, M. (1998) · doi:10.5555/972764.972768 · Cited by 350
Managing Diversity in Regression Ensembles
Gavin Brown, B., Wyatt, J., & Peter Tiňo, B. (2005) · Research Explorer (The University of Manchester) · doi:10.5555/1046920.1194899 · Cited by 353
Rethinking Eliminative Connectionism
Marcus, G. (1998) · Cognitive Psychology · doi:10.1006/cogp.1998.0694 · Cited by 354
Active learning for logistic regression: an evaluation
Schein, A. I., & Ungar, L. (2007) · Machine Learning · doi:10.1007/s10994-007-5019-5 · Cited by 358
Neural networks in geophysical applications
van der Baan, M., & Jutten, C. (2000) · Geophysics · doi:10.1190/1.1444797 · Cited by 347
Porosity prediction: Supervised-learning of thermal history for direct laser deposition
Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M. A., & Bian, L. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.04.001 · Cited by 339
Analysis of decision boundaries in linearly combined neural classifiers
Tumer, K., & Ghosh, J. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00085-2 · Cited by 334
Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
Cawley, G. C., & Talbot, N. L. C. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.07.002 · Cited by 336
Mjalli, F. S., Al‐Asheh, S., & Alfadala, H. E. (2006) · Journal of Environmental Management · doi:10.1016/j.jenvman.2006.03.004 · Cited by 324
Machine learning approach for pavement performance prediction
Marcelino, P., Antunes, M. D. L., Fortunato, E., & Gomes, M. C. (2019) · International Journal of Pavement Engineering · doi:10.1080/10298436.2019.1609673 · Cited by 342
Six principles for biologically based computational models of cortical cognition
O’Reilly, R. C. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01241-8 · Cited by 322
Using Iterated Bagging to Debias Regressions
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1017934522171 · Cited by 330
Nguyen, N., & Cripps, A. (2001) · Journal of Real Estate Research · doi:10.1080/10835547.2001.12091068 · Cited by 314
Decoding brain states from fMRI connectivity graphs
Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P. O., & Ville, D. V. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.081 · Cited by 321
A theoretical and experimental analysis of linear combiners for multiple classifier systems
Fumera, G., & Roli, F. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.109 · Cited by 312
Learning model trees from evolving data streams
Ikonomovska, E., da Gama, J. M. P., & Džeroski, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0201-y · Cited by 304
Schwenk, H., & Bengio, Y. (2000) · Neural Computation · doi:10.1162/089976600300015178 · Cited by 299
Wiskott, L., Rasch, M. J., & Kempermann, G. (2006) · Hippocampus · doi:10.1002/hipo.20167 · Cited by 301
OpitzDavid, & MaclinRichard (1999) · doi:10.5555/3013545.3013549 · Cited by 293
A fast and accurate face detector based on neural networks
Feraund, R., Bernier, O., Viallet, J. E., & Collobert, M. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.899945 · Cited by 291
Ensembles of Learning Machines
Valentini, G., & Masulli, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45808-5_1 · Cited by 290
da Gama, J. M. P. (2004) · Machine Learning · doi:10.1023/b:mach.0000027782.67192.13 · Cited by 287
Mean Squared Error, Deconstructed
Hodson, T., Over, T. M., & Foks, S. S. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002681 · Cited by 281
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Gigerenzer, G. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01094.x · Cited by 297
SALMON: survival analysis learning with multi-omics neural networks on breast cancer
Huang, Z., Zhan, X., Xiang, S., Johnson, T. S., Helm, B., Yu, C. Y., Zhang, J., Salama, P., Rizkalla, M., Han, Z., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00166 · Cited by 262
“Using eigengenes as features can be considered as bias/variance (error/complexity) trade-off in machine learning (Weigend et al., 1990; Geman et al., 1992), which simplifies the networks significantly.”
Randomizing Outputs to Increase Prediction Accuracy
Breiman, L. (2000) · Machine Learning · doi:10.1023/a:1007682208299 · Cited by 280
Applications of artificial neural networks in chemical engineering
Himmelblau, D. M. (2000) · Korean Journal of Chemical Engineering · doi:10.1007/bf02706848 · Cited by 274
Neural Network Exploration Using Optimal Experiment Design
Cohn, D. A. (1996) · Neural Networks · doi:10.1016/0893-6080(95)00137-9 · Cited by 278
Combustion machine learning: Principles, progress and prospects
Ihme, M., Chung, W. T., & Mishra, A. (2022) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2022.101010 · Cited by 300
Simultaneous training of negatively correlated neural networks in an ensemble
Liu, Y., & Yao, X. (1999) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.809027 · Cited by 267
Ibrahim, I. A., & Khatib, T. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.02.006 · Cited by 267
Ensemble with neural networks for bankruptcy prediction
Kim, M.-J., & Kang, D.-K. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.10.012 · Cited by 276
Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks
Chen, H., Dou, Q., Wang, X., Qin, J., & Heng, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10140 · Cited by 264
Feature subset selection using the wrapper method: overfltting and dynamic search space topology
Kohavi, R., & Sommerfield, D. (1995) · Knowledge Discovery and Data Mining · Cited by 260
Structured statistical models of inductive reasoning
Kemp, C., & Tenenbaum, J. B. (2009) · Psychological Review · doi:10.1037/a0014282 · Cited by 259
Gradient descent learning algorithm overview: a general dynamical systems perspective
Baldi, P. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363438 · Cited by 260
Heuristics as adaptive decision strategies in management: HEURISTICS IN MANAGEMENT
Artinger, F., Petersen, M., Gigerenzer, G., & Weibler, J. (2015) · Journal of Organizational Behavior · doi:10.1002/job.1950 · Cited by 255
Piotrowski, A., & Napiorkowski, J. J. (2012) · Journal of Hydrology · doi:10.1016/j.jhydrol.2012.10.019 · Cited by 266
Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2020) · Energies · doi:10.3390/en13020391 · Cited by 256
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
Spinner, T., Schlegel, U., Schäfer, H., & El‐Assady, M. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934629 · Cited by 291
Factorial Hidden Markov Models.
Zoubin Ghahramani, A., & Jordan, M. I. (1996) · doi:10.21236/ada307097 · Cited by 256
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
Valentini, G., & Dietterich, T. G. (2004) · doi:10.5555/1005332.1016783 · Cited by 251
Ensemble Learning Using Decorrelated Neural Networks
Rosen, B. E. (1996) · Connection Science · doi:10.1080/095400996116820 · Cited by 247
Data splitting for artificial neural networks using SOM-based stratified sampling
May, R., Maier, H. R., & Dandy, G. C. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.11.009 · Cited by 246
Accelerator-friendly neural-network training: Learning variations and defects in RRAM crossbar
Chen, L., Li, J., Chen, Y., Deng, Q., Shen, J., Liang, X., & Jiang, L. (2017) · Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 · doi:10.23919/date.2017.7926952 · Cited by 245
Robust decision trees: removing outliers from databases
John, G. H. (1995) · Cited by 244
Balancing Accuracy and Parsimony in Genetic Programming
Zhang, B., & Mühlenbein, H. (1995) · Evolutionary Computation · doi:10.1162/evco.1995.3.1.17 · Cited by 242
Model selection approaches for non-linear system identification: a review
Hong, X., Mitchell, R., Chen, S., Harris, C. J., Li, K., & Irwin, G. W. (2008) · International Journal of Systems Science · doi:10.1080/00207720802083018 · Cited by 244
Multiple Comparisons in Induction Algorithms
Jensen, D. D., & Cohen, P. R. (2000) · Machine Learning · doi:10.1023/a:1007631014630 · Cited by 241
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Schaal, S., Atkeson, C. G., & Vijayakumar, S. (2002) · Applied Intelligence · doi:10.1023/a:1015727715131 · Cited by 241
Ecological Rationality: Fast-and-Frugal Heuristics for Managerial Decision Making under Uncertainty
Luan, S., Reb, J. M., & Gigerenzer, G. (2019) · Academy of Management Journal · doi:10.5465/amj.2018.0172 · Cited by 251
Wahba, G., Wang, Y., Gu, C., Klein, R., & Klein, R. (1995) · The Annals of Statistics · doi:10.1214/aos/1034713638 · Cited by 228
Friedman, J. H. (1996) · Cited by 233
Park, Y.-S., & Lek, S. (2016) · Developments in environmental modelling · doi:10.1016/b978-0-444-63623-2.00007-4 · Cited by 258
Decoding Trajectories from Posterior Parietal Cortex Ensembles
Mulliken, G. H., Musallam, S., & Andersen, R. A. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.1463-08.2008 · Cited by 231
Cavalieri, S., Maccarrone, P., & Pinto, R. (2003) · International Journal of Production Economics · doi:10.1016/j.ijpe.2003.08.005 · Cited by 245
Generalization error of ensemble estimators
Ueda, N., & Nakano, R. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1996.548872 · Cited by 235
Generative artificial intelligence and evaluating strategic decisions
Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2024) · Strategic Management Journal · doi:10.1002/smj.3677 · Cited by 288
Variability in motor learning: relocating, channeling and reducing noise
Cohen, R. G., & Sternad, D. (2008) · Experimental Brain Research · doi:10.1007/s00221-008-1596-1 · Cited by 224
Optimal ensemble averaging of neural networks
Naftaly, U., Intrator, N., & Horn, D. (1997) · Network Computation in Neural Systems · doi:10.1088/0954-898x_8_3_004 · Cited by 227
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
Cawley, G. C., & Talbot, N. L. C. (2007) · UEA Digital Repository (University of East Anglia) · doi:10.5555/1248659.1248690 · Cited by 226
Hierarchical Neural Networks for Image Interpretation
Behnke, S. (2003) · Lecture notes in computer science · doi:10.1007/b11963 · Cited by 227
One-shot learning by inverting a compositional causal process
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2013) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 223
Active Learning with Statistical Models.
Cohn, D. A., Zoubin Ghahramani, A., & Jordan, M. I. (1995) · doi:10.21236/ada295617 · Cited by 222
Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs
Cawley, G. C. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246634 · Cited by 221
Bias-corrected random forests in regression
Zhang, G., & Lu, Y. (2012) · Journal of Applied Statistics · doi:10.1080/02664763.2011.578621 · Cited by 196
Support vector learning for fuzzy rule-based classification systems
Chen, Y., & Wang, J. Z. (2003) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2003.819843 · Cited by 220
COST ESTIMATION PREDICTIVE MODELING: REGRESSION VERSUS NEURAL NETWORK
Smith, A. E., & Mason, A. K. (1997) · The Engineering Economist · doi:10.1080/00137919708903174 · Cited by 222
DATA STREAM MINING A Practical Approach
Bifet, A., & Kirkby, R. (2009) · Cited by 221
Gigerenzer, G., & Marewski, J. N. (2014) · Journal of Management · doi:10.1177/0149206314547522 · Cited by 227
Encoding Multielement Scenes: Statistical Learning of Visual Feature Hierarchies.
Fiser, J., & Aslin, R. Ν. (2005) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.134.4.521 · Cited by 223
Zur, R. M., Jiang, Y., Pesce, L. L., & Drukker, K. (2009) · Medical Physics · doi:10.1118/1.3213517 · Cited by 207
Adoption of Machine Learning Techniques in Ecology and Earth Science
Thessen, A. (2016) · One Ecosystem · doi:10.3897/oneeco.1.e8621 · Cited by 211
What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation
Lawrence, S., Giles, C. L., & Tsoi, A. C. (1998) · University Libraries (University of Maryland) · Cited by 208
Voice Conversion Using Partial Least Squares Regression
Helander, E. E., Virtanen, T. I., Nurminen, J., & Gabbouj, M. (2010) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2010.2041699 · Cited by 207
Metadata
- Authors
- Geman, S., Bienenstock, E., & Doursat, R.
- Affiliations
- Brown University (US)
- ESPCI Paris (FR)
- ORCID
- Doursat, René
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1992-01
- Volume
- 4
- Issue
- 1
- Pages
- 1-58
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2076118331
- Cited by
- 3,578
- References
- 96
- Citations by year
- 2026: 125 · 2025: 131 · 2024: 115 · 2023: 128 · 2022: 148 · 2021: 201 · 2020: 181 · 2019: 142 · 2018: 126 · 2017: 97 · 2016: 115 · 2015: 99 · 2014: 103 · 2013: 82 · 2012: 110
- FWCI
- 60.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- e-space (Manchester Metropolitan University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- bias-variance trade-off, feedforward neural networks, backpropagation, nonparametric regression, nonparametric inference, handwritten digit recognition, machine perception, neural network representations, statistical learning
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