The strength of weak learnability

Probability Inequalities for sums of Bounded Random Variables

Hoeffding, W. (1994) · Springer series in statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,683

Probability Inequalities for Sums of Bounded Random Variables

Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500830 · Cited by 4,869

A Theory of the Learnable

Valiant, L. G. (1984) · ACM eBooks · doi:10.1145/3749740.3749746 · Cited by 4,211

A theory of the learnable

Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279

Queries and Concept Learning

Angluin, D. (1988) · Machine Learning · doi:10.1023/a:1022821128753 · Cited by 1,918

Learnability and the Vapnik-Chervonenkis dimension

Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839

Probability Inequalities for Sums of Bounded Random Variables

Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.2307/2282952 · Cited by 1,168

Occam's Razor

Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987) · Information Processing Letters · doi:10.1016/0020-0190(87)90114-1 · Cited by 1,054

Learning Decision Lists

Rivest, R. L. (1987) · Machine Learning · doi:10.1023/a:1022607331053 · Cited by 894

Finding patterns common to a set of strings

Angluin, D. (1980) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(80)90041-0 · Cited by 609

The individual borrowers recognition: Single and ensemble trees

Chrzanowska, M., Alfaro, E., & Witkowska, D. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.07.048 · Cited by 23

Learning DNF by approximating inclusion-exclusion formulae

Tarui, J., & Tsukiji, T. (2003) · IEEE Conference on Computational Complexity (CCC) · doi:10.1109/ccc.1999.766279 · Cited by 24

Improving OCR performance using character degradation models and boosting algorithm

Mao, J., & Mohiuddin, K. M. (1997) · Pattern Recognition Letters · doi:10.1016/s0167-8655(97)00137-2 · Cited by 23

Comparison of ophthalmic beta-blocking agents.

Lesar, T. S. (1987) · PubMed · Cited by 23

Utterance-level boosting of HMM speech recognizers

Meyer, C. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5743666 · Cited by 23

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Elmousalami, H. H., Elmesalami, H. H., Maxi, M., Farid, A. A. K. M., & Elshaboury, N. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100527 · Cited by 23

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Oliveira-Filho, A., Comeau, M., Cave, J., Nasr, C., Côté, P., & Tahan, A. S. (2024) · Energies · doi:10.3390/en18010059 · Cited by 28

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Ucci, G., Ferrara, A., Pallottini, A., & Gallerani, S. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty804 · Cited by 22

KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization

Zhang, S., & Qiao, H. (2020) · Analytical Biochemistry · doi:10.1016/j.ab.2020.113995 · Cited by 23

Surface generalizations

Goldberg, A. E. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199268511.003.0002 · Cited by 25

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Khalil, A. A., Liu, Z., Salah, A., Fathalla, A., & Ali, A. (2022) · IEEE Access · doi:10.1109/access.2022.3210032 · Cited by 22

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Ren, D., Qu, F., Lv, K., Zhang, Z., Xu, H., & Wang, X. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.109 · Cited by 22

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Khot, S., & Saket, R. (2010) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2010.06.010 · Cited by 25

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Reboiro‐Jato, M., Díaz, F., Glez‐Peña, D., & Fdez‐Riverola, F. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.01.002 · Cited by 22

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Wahla, A. H., Chen, L., Wang, Y., Chen, R., & Wu, F. (2019) · IEEE Access · doi:10.1109/access.2019.2950989 · Cited by 33

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Zitouni, I., Kuo, H.-K. J., & Lee, C. (2003) · Speech Communication · doi:10.1016/s0167-6393(03)00103-1 · Cited by 22

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Hou, X., Liu, C., & Tan, T. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.166 · Cited by 22

Action coverage formulation for power optimization in body sensor networks

Ghasemzadeh, H. G., Guenterberg, E., Gilani, K., & Jafari, R. (2008) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2008.4483992 · Cited by 22

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Neumann, A., Holstein, J., Gall, J. L., & Lepage, É. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.06.001 · Cited by 22

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Holopainen, M., & Sarlin, P. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2778530 · Cited by 25

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Wu, Y. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112188 · Cited by 21

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Liu, Y. (2011) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/iwaci.2011.6160030 · Cited by 21

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Liu, Y. (2011) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc.2011.6061781 · Cited by 21

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Sanz, J., Paternain, D., Galar, M., Fernández, J., Díez, D. R., & Otano, T. B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.02.011 · Cited by 22

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Elsayad, A. M., Al‐Dhaifallah, M., & Nassef, A. M. (2018) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd.2018.8570553 · Cited by 21

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Elliott, D. L., & Anderson, C. W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3089425 · Cited by 23

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Uikey, R., & Gyanchandani, M. (2019) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces45898.2019.9002129 · Cited by 21

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Stavropoulos, G., van Vorstenbosch, R., van Schooten, F., & Smolinska, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.14589-5 · Cited by 22

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Huang, C. Y., & Dai, H. L. (2021) · Data Science in Finance and Economics · doi:10.3934/dsfe.2021002 · Cited by 21

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Gomez-Carmona, O., Casado-Mansilla, D., Lopez-de-Ipina, D., & Garcia-Zubia, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3118845 · Cited by 21

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Węgier, W., Koziarski, M., & Woźniak, M. (2022) · IEEE Access · doi:10.1109/access.2022.3149914 · Cited by 20

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Ahmadzadeh, M., Cosco, T. D., Best, J. R., Christie, G. J., & DiPaola, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0280029 · Cited by 24

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Tan, S., Luo, R., Zhou, Y., Xu, H., Song, D., Ze, T. H., Yang, T., & Nie, Y. (2012) · Journal of Chemometrics · doi:10.1002/cem.1423 · Cited by 21

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Wang, W., Wang, Y., Chen, F., & Sowmya, A. (2013) · Proceedings · doi:10.1109/wacv.2013.6475037 · Cited by 21

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Flórez‐López, R., & JERONIMO, J. M. R. (2013) · Journal of the Operational Research Society · doi:10.1057/jors.2013.119 · Cited by 21

Data mining with ensembles of fuzzy decision trees

Marsala, C. (2009) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2009.4938670 · Cited by 21

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Armano, G., & Tamponi, E. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.017 · Cited by 20

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Bian, X., Diwu, P., Zhang, C., Lin, L., Chen, G., Tan, X., Guo, Y., & Cheng, B. (2018) · Analytica Chimica Acta · doi:10.1016/j.aca.2018.01.013 · Cited by 21

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Landers, J. R., & Duperrouzel, B. (2018) · IEEE Transactions on Games · doi:10.1109/tg.2018.2841057 · Cited by 20

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Fiorentini, N., Maboudi, M., Leandri, P., & Losa, M. (2021) · Sensors · doi:10.3390/s21103377 · Cited by 21

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Mehmood, F., Khan, M. U. G., Asim, M. N., Shahzadi, R., Mehmood, A., & Mahmood, W. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111559 · Cited by 21

A multi-attribute classification fusion system for insulated gate bipolar transistor diagnostics

Tamilselvan, P., Wang, P., & PECHT, M. G. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.04.011 · Cited by 21

Creating an ensemble of diverse support vector machines using Adaboost

Lima, N. H. C., Neto, A. D. D., & de Melo, J. D. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178915 · Cited by 21

Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects

Shan, Y., Han, F., Sawhney, H., & Kumar, R. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.168 · Cited by 21

S-AdaBoost and pattern detection in complex environment

Jiang, J. L., & Loe, K.-F. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211383 · Cited by 21

Neural Networks to Diagnose the Parkinson’s Disease

Can, M. (2013) · Southeast Europe Journal of Soft Computing · doi:10.21533/scjournal.v2i1.48 · Cited by 21

Multiple Classifier System with Feature Grouping for Intrusion Detection: Mutual Information Approach

Chan, A. P. F., Ng, W. W. Y., Yeung, D. S., & Tsang, E. C. C. (2005) · Lecture notes in computer science · doi:10.1007/11553939_21 · Cited by 21

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Li, Y., Li, Z., & Li, M. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109863 · Cited by 26

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Zhao, S., Zhou, D., Wang, H., Chen, D., & Yu, L. (2025) · Applied Sciences · doi:10.3390/app15031231 · Cited by 20

Learning Abductive Reasoning Using Random Examples

Juba, B. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10099 · Cited by 20

Target shift awareness in balanced ensemble learning

Liu, Y. (2011) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2011.6163133 · Cited by 20

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Zeng, J., Zhang, M., & Lin, S.-B. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.016 · Cited by 22

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Liew, B. X. W., Peolsson, A. L. C., Rügamer, D., Wibault, J., Löfgren, H., Dedering, Å., Zsigmond, P., & Falla, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-73740-7 · Cited by 20

Using machine learning tools to investigate factors associated with trends in ‘no-shows’ in outpatient appointments

Incze, E., Holborn, P. L., Higgs, G., & Ware, A. (2020) · Health & Place · doi:10.1016/j.healthplace.2020.102496 · Cited by 21

Log interpretation for lithofacies classification with a robust learning model using stacked generalization

He, M., Gu, H., & Xue, J. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.110541 · Cited by 20

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Lee, S., Tseng, C.-H., Yang, H.-Y., Jin, X., Jiang, Q., Pu, B., Hu, W.-H., Liu, D., Huang, Y., & Zhao, N. (2022) · Entropy · doi:10.3390/e24050617 · Cited by 20

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Fang, J., Gong, B., & Caers, J. (2022) · Engineering · doi:10.1016/j.eng.2022.04.015 · Cited by 20

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Kalampokas, Τ., Tziridis, K., Kalampokas, N., Nikolaou, A., Vrochidou, Ε., & Papakostas, G. A. (2023) · IEEE Access · doi:10.1109/access.2023.3274669 · Cited by 21

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Sun, J., Sun, Y., Shen, A., Li, Y., Gao, X., & Lu, B. (2024) · npj Aging · doi:10.1038/s41514-023-00129-x · Cited by 20

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Duan, R., Yan, C., Wang, J., & Jiang, C. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102461 · Cited by 20

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de Ridder, D., Duin, R. P. W., Egmont-Petersen, M., A, L. J. V. V., & Verbeek, P. W. (2003) · Advances in imaging and electron physics · doi:10.1016/s1076-5670(03)80019-8 · Cited by 20

Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression

Luo, R., Tan, S., Zhou, Y., Liu, S., Xu, H., Song, D., Cui, Y., Fu, H., & Yang, T. (2013) · Journal of Chemometrics · doi:10.1002/cem.2518 · Cited by 20

Islanding detection for PV and DFIG using decision tree and AdaBoost algorithm

Madani, S. S., Abbaspour, A., Beiraghi, M., Dehkordi, P. Z., & Ranjbar, A. M. (2012) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2012.6465818 · Cited by 20

Fast AdaBoost training using weighted novelty selection

Seyedhosseini, M., Paiva, A. R. C., & Taşdizen, T. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033366 · Cited by 20

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

Alfaro-Cid, E., Castillo, P. Á., Esparcia, A. I., Sharman, K. C., Merelo, J. J., Prieto, A., Mora, A. M., & Laredo, J. L. J. (2008) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2008.4631188 · Cited by 20

Shrinkage Degree in L₂-Rescale Boosting for Regression

Xu, L., Lin, S.-B., Wang, Y., & Xu, Z. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2560224 · Cited by 21

Prediction of pork storage time using Fourier transform near infrared spectroscopy and Adaboost‐ULDA

Wu, X., Fu, H., Tian, X., Wu, B., & Sun, J. (2017) · Journal of Food Process Engineering · doi:10.1111/jfpe.12566 · Cited by 22

Classification by pairwise coupling of imprecise probabilities

Quost, B., & Destercke, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.019 · Cited by 19

Spam classification: a comparative analysis of different boosted decision tree approaches

Trivedi, S. K., & Panigrahi, P. K. (2018) · Journal of Systems and Information Technology · doi:10.1108/jsit-11-2017-0105 · Cited by 20

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Queipo, N. V., & Nava, E. (2019) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-019-02325-4 · Cited by 20

A New Reliable Cancer Diagnosis Method Using Boosted Fuzzy Classifier with a SWEEP Operator Method

Takahashi, H., & Honda, H. (2005) · JOURNAL OF CHEMICAL ENGINEERING OF JAPAN · doi:10.1252/jcej.38.763 · Cited by 20

Exploring margin setting for good generalization in multiple class discrimination

Caulfield, H. J., & Heidary, K. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.01.009 · Cited by 20

Boosting HMM acoustic models in large vocabulary speech recognition

Meyer, C., & Schramm, H. (2005) · Speech Communication · doi:10.1016/j.specom.2005.09.009 · Cited by 20

A mixture of experts committee machine to design compensators for intensity modulated radiation therapy

Goodband, J., Haas, O. C. L., & Mills, J. A. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.03.018 · Cited by 20

Boosted ARTMAP: Modifications to fuzzy ARTMAP motivated by boosting theory

Verzi, S., Heileman, G. L., & Georgiopoulos, M. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.08.013 · Cited by 20

A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes

Yu, J., & Xi, L. (2008) · International Journal of Production Research · doi:10.1080/00207540801942208 · Cited by 20

Customer price sensitivities in competitive insurance markets

Verschuren, R. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117133 · Cited by 19

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Shaikh, S. A., Taufique, M. F. N., Balusu, K., Kulkarni, S. S., Hale, F., Oleson, J., Devanathan, R., & Soulami, A. (2024) · Applied Composite Materials · doi:10.1007/s10443-024-10218-z · Cited by 19

Prediction of the traffic incident duration using statistical and machine-learning methods: A systematic literature review

Korkmaz, H. E., & Ertürk, M. A. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123621 · Cited by 21

Circular rubber aggregate CFST stub columns under axial compression: prediction and reliability analysis

Megahed, K., Mahmoud, N. S., & Abd-Rabou, S. E. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-74990-5 · Cited by 20

A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback

Lee, K. S., & Croft, W. B. (2013) · Information Processing & Management · doi:10.1016/j.ipm.2013.01.001 · Cited by 19

Concurrent optimization of multiple base learners in neural network ensembles: An adaptive niching differential evolution approach

Huang, T., Duan, D.-T., Gong, Y., Ye, L., Ng, W. W. Y., & Zhang, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.020 · Cited by 19

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Iannelli, M., Rahman, M. R., Choi, N., & Wang, L. (2020) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft48620.2020.9165317 · Cited by 19

On the value of filter feature selection techniques in homogeneous ensembles effort estimation

Hosni, M., Idri, A., & Abran, A. (2021) · Journal of Software Evolution and Process · doi:10.1002/smr.2343 · Cited by 19

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

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Albuquerque, P. H. M., Peng, Y., & da Silva, J. P. F. (2022) · Journal of Forecasting · doi:10.1002/for.2894 · Cited by 23

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Hao, J., He, F., Ma, F., Zhang, S., & Zhang, X. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05286-6 · Cited by 25

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de Souza, L. P. B., Ferreira, G. R. B., Camerini, I. G., Correia, T. D. M., Ribeiro, M. G. D. C., Hidalgo, J. A. S., João, B. L. D. D. S., Llerena, R., Kubrusly, A. C., Ayala, H. V. H., et al. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.211882 · Cited by 19

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Parvïn, H., Alizadeh, H., Bidgoli, B. M., & Analoui, M. (2008) · Networked Computing and Advanced Information Management · doi:10.1109/ncm.2008.228 · Cited by 19

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Apolloni, B., Brega, A., Malchiodi, D., Palmas, G., & Zanaboni, A. M. (2006) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2006.878987 · Cited by 19

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Machine Learning (Lecture notes 12)

Rivest, R. L. (1994)

Boosting Ideas pageMehryar Mohri- Foundations of Machine Learning

Mohri, M., & Preliminaries, A. (2015)

The design and analysis of efficient learning algorithms

Schapire, R. E. (1992) · Cited by 97

Topics in computational learning theory and graph algorithms

Raymond Acton Board (1990) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)

From average case complexity to improper learning complexity

Daniely, A., Linial, N., & Shalev‐Shwartz, S. (2014) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2591796.2591820 · Cited by 70