The changing science of machine learning
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The field of data science has historically focused on certain tasks and evaluation metrics that are not applicable to some of climate science's biggest needs.
“Second, the field of data science has historically focused on certain tasks and evaluation metrics 2 that are not applicable to some of climate science's biggest needs.”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [2]
Traditional data science and machine learning have relied on attribute-value data as input to most learning models.
“Traditional data science, and machine learning specifically, has relied on attribute-value data as input to most learning models.2”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [2]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
(2013) · Springer eBooks · Cited by 2,625 · see citing works
Machine learning: An artificial intelligence approach
Hunt, E. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90037-8 · Cited by 1,375 · see citing works
Explanation-Based Learning: An Alternative View
DEJONG, G. F., & Mooney, R. J. (1986) · Machine Learning · doi:10.1023/a:1022898111663 · Cited by 839 · see citing works
Chunking in Soar: The Anatomy of a General Learning Mechanism
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986) · Machine Learning · doi:10.1023/a:1022639103969 · Cited by 509 · see citing works
Chunking in Soar: The anatomy of a general learning mechanism
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986) · Machine Learning · doi:10.1007/bf00116249 · Cited by 287 · see citing works
Simon, H. A. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_2 · Cited by 261 · see citing works
Machine learning as an experimental science
Langley, P. (1988) · Machine Learning · doi:10.1007/bf00115008 · Cited by 187 · see citing works
Self-citationAutomated Refinement of First-Order Horn-Clause Domain Theories
Richards, B. L., & Mooney, R. J. (1995) · Machine Learning · doi:10.1023/a:1022611224557 · Cited by 138 · see citing works
Toward a Unified Science of Machine Learning
Langley, P. (1989) · Machine Learning · doi:10.1023/a:1022689616458 · Cited by 39 · see citing works
Self-citationResearch Papers in Machine Learning
Langley, P. (1987) · Machine Learning · doi:10.1023/a:1022603230145 · Cited by 12 · see citing works
Self-citationEditorial: Human and Machine Learning
Langley, P. (1986) · Machine Learning · doi:10.1023/a:1022854429410 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Machine Learning: The art and science of algorithms that make sense of data
Flach, P. A. (2012) · Cited by 694 · see citing works
Machine learning for medical imaging: methodological failures and recommendations for the future
Varoquaux, G. P., & Cheplygina, V. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00592-y · Cited by 720 · see citing works
An Artificial Sensory Neuron with Tactile Perceptual Learning
Wan, C., Chen, G., Fu, Y., Wang, M., Matsuhisa, N., Pan, S., Pan, L., Yang, H., Wan, Q., Zhu, L., et al. (2018) · Advanced Materials · doi:10.1002/adma.201801291 · Cited by 418 · see citing works
Moein, M. M., Saradar, A., Rahmati, K., Mousavinejad, S. H. G., Bristow, J., Aramali, V., & Karakouzian, M. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105444 · Cited by 419 · see citing works
A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science
Faghmous, J. H., & Kumar, V. (2014) · Big Data · doi:10.1089/big.2014.0026 · Cited by 223 · see citing works
Deep learning-based cardiovascular image diagnosis: A promising challenge
Wong, K. K. L., Fortino, G., & Abbott, D. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.09.047 · Cited by 212 · see citing works
Introduction to machine learning
Vieira, S., Pinaya, W. H. L., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00001-8 · Cited by 203 · see citing works
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement
Hernández‐Orallo, J. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9505-7 · Cited by 208 · see citing works
Algorithmic realism: expanding the boundaries of algorithmic thought
Green, B., & Viljoen, S. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372840 · Cited by 161 · see citing works
Machine-learning based design of active composite structures for 4D printing
Hamel, C. M., Roach, D. J., Long, K. N., Demoly, F., Dunn, M. L., & Qi, H. J. (2019) · Smart Materials and Structures · doi:10.1088/1361-665x/ab1439 · Cited by 159 · see citing works
Liu, Z., Wu, D., Liu, Y., Han, Z., Lun, L., Gao, J., Jin, G., & Cao, G. (2019) · Energy Exploration & Exploitation · doi:10.1177/0144598718822400 · Cited by 157 · see citing works
Instance spaces for machine learning classification
Muñoz, M. A., Villanova, L., Baatar, D., & Smith‐Miles, K. (2017) · Machine Learning · doi:10.1007/s10994-017-5629-5 · Cited by 153 · see citing works
Computational design for 4D printing of topology optimized multi-material active composites
Athinarayanarao, D., Prod’hon, R., Chamoret, D., Qi, H. J., Bodaghi, M., André, J., & Demoly, F. (2023) · npj Computational Materials · doi:10.1038/s41524-022-00962-w · Cited by 150 · see citing works
Wagstaff, K. (2012) · arXiv · doi:10.48550/arxiv.1206.4656 · Cited by 148 · see citing works
Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes?
Li, G., Dong, Y., & Reetz, M. T. (2019) · Advanced Synthesis & Catalysis · doi:10.1002/adsc.201900149 · Cited by 123 · see citing works
Okewu, E., Adewole, P., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.1922847 · Cited by 137 · see citing works
Morgenstern, J., Rosella, L. C., Daley, M., Goel, V., Schünemann, H. J., & Piggott, T. (2021) · BMC Public Health · doi:10.1186/s12889-020-10030-x · Cited by 112 · see citing works
Artificial Intelligence Techniques for Smart City Applications
Luckey, D., Fritz, H., Legatiuk, D., Dragos, K., & Smarsly, K. (2020) · Lecture notes in civil engineering · doi:10.1007/978-3-030-51295-8_1 · Cited by 102 · see citing works
Kausik, A. K., Rashid, A. B., Baki, R., & Maktum, M. M. J. (2025) · Array · doi:10.1016/j.array.2025.100393 · Cited by 90 · see citing works
Unplugged Activities in the Context of AI
Lindner, A., Seegerer, S., & Romeike, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33759-9_10 · Cited by 74 · see citing works
A system for educational and vocational guidance in Morocco: Chatbot E-Orientation
Zahour, O., Benlahmar, E. H., Eddaoui, A., Ouchra, H., & Hourrane, O. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.079 · Cited by 70 · see citing works
Unveiling the Mind of the Machine
Clegg, M., Hofstetter, R., de Bellis, E., & Schmitt, B. H. (2023) · Journal of Consumer Research · doi:10.1093/jcr/ucad075 · Cited by 71 · see citing works
Freiesleben, J., Keim, J., & Grutsch, M. (2020) · Quality and Reliability Engineering International · doi:10.1002/qre.2579 · Cited by 65 · see citing works
Towards UCI+: A mindful repository design
Macià, N., & Bernadó-Mansilla, E. (2013) · Information Sciences · doi:10.1016/j.ins.2013.08.059 · Cited by 66 · see citing works
Neuromorphic Devices for Bionic Sensing and Perception
Zeng, M., He, Y., Zhang, C., & Wan, Q. C. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.690950 · Cited by 59 · see citing works
Wagle, N., Acharya, T. D., & Lee, D. H. (2020) · Sensors and Materials · doi:10.18494/sam.2020.2953 · Cited by 57 · see citing works
When not to use machine learning: A perspective on potential and limitations
Carbone, M. R. (2022) · MRS Bulletin · doi:10.1557/s43577-022-00417-z · Cited by 53 · see citing works
The cognitive systems paradigm
Langley, P. (2012) · Cited by 51 · see citing works
Self-citationMulti-omics based artificial intelligence for cancer research
Li, L., Sun, M., Wang, J., & Wan, S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.005 · Cited by 47 · see citing works
Owais, M., & Idriss, L. K. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.137393 · Cited by 45 · see citing works
Red light crossing violations modelling using deep learning and variance-based sensitivity analysis
Owais, M., & Sayed, M. A. E. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126258 · Cited by 44 · see citing works
Han, X., Zheng, X., Wang, Y., Sun, X., Xiao, Y., Tang, Y., & Qin, W. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2018.12.11 · Cited by 44 · see citing works
Overview of Artificial Neural Networks
Rosa, J. P. S., Guerra, D. J. D., Horta, N. C. G., Martins, R. M. F., & Lourenço, N. C. C. (2019) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-030-35743-6_3 · Cited by 40 · see citing works
Sun, Z. Y., Wang, X., Huang, H., Yang, Y., & Wu, Z. (2024) · Structures · doi:10.1016/j.istruc.2024.106516 · Cited by 36 · see citing works
Oh, Y., Ransikarbum, K., Busogi, M., Kwon, D., & Kim, N. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.03.020 · Cited by 31 · see citing works
The Limits of Abstract Evaluation Metrics
Olteanu, A., Talamadupula, K., & Varshney, K. R. (2017) · ACM Web Science Conference (WebSci) · doi:10.1145/3091478.3098871 · Cited by 30 · see citing works
Design Approaches of High‐Entropy Alloys Using Artificial Intelligence: A Review
Eldabah, N. M., Pratap, A., Pandey, A., Sardana, N., Sidhu, S. S., & Gepreel, M. A. H. (2025) · Advanced Engineering Materials · doi:10.1002/adem.202402504 · Cited by 29 · see citing works
Nezami-Alanagh, E., & Gallego, P. P. F. (2023) · Plants · doi:10.3390/plants12020323 · Cited by 29 · see citing works
Distributed and weighted extreme learning machine for imbalanced big data learning
Wang, Z., Xin, J., Yang, H., Tian, S., Yu, G., Xu, C., & Yao, Y. (2017) · Tsinghua Science & Technology · doi:10.23919/tst.2017.7889638 · Cited by 27 · see citing works
Introduction to machine learning
Burzykowski, T., Rousseau, A.-J., Geubbelmans, M., & Valkenborg, D. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.02.005 · Cited by 27 · see citing works
Significance tests or confidence intervals: which are preferable for the comparison of classifiers?
Berrar, D., & Lozano, J. A. (2012) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2012.680252 · Cited by 25 · see citing works
A hybrid flexible gas sensory system with perceptual learning
Lu, Q., Sun, F., Dai, Y., Wang, Y., Liu, L., Wang, Z., Wang, S., & Zhang, T. (2021) · Nano Research · doi:10.1007/s12274-021-3496-7 · Cited by 23 · see citing works
Learning from automatically labeled data: case study on click fraud prediction
Berrar, D. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0827-6 · Cited by 23 · see citing works
Towards a Chatbot for educational and vocational guidance in Morocco: Chatbot E-Orientation
Zahour, O. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/237922020 · Cited by 22 · see citing works
Web genre classification with methods for structured output prediction
Madjarov, G., Vidulin, V., Dimitrovski, I., & Kocev, D. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.009 · Cited by 22 · see citing works
Toma, C., Popa, M., Iancu, B., Doinea, M., Pascu, A., & Ioan-Dutescu, F. (2022) · Electronics · doi:10.3390/electronics11213507 · Cited by 22 · see citing works
Borunda, M., Ramírez, A., Garduño, R., García‐Beltrán, C. D., & Mijarez, R. (2023) · Energies · doi:10.3390/en16237915 · Cited by 19 · see citing works
Big Data in Climate Change Research: opportunities and Challenges
Mangal, P., Rajesh, A., & Misra, R. (2020) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem48762.2020.9160174 · Cited by 19 · see citing works
Sterne, J., & Razlogova, E. (2021) · Cultural Studies · doi:10.1080/09502386.2021.1895247 · Cited by 19 · see citing works
Malekjani, N., & Jafari, S. M. (2022) · Food and Bioprocess Technology · doi:10.1007/s11947-022-02791-7 · Cited by 19 · see citing works
Künstliche Intelligenz in der Hochschulbildung
Schmohl, T., Watanabe, A., & Schelling, K. (2023) · Hochschulbildung: Lehre und Forschung · doi:10.14361/9783839457696 · Cited by 37 · see citing works
Integration of Blockchain and Artificial Intelligence in Smart City Perspectives
Krishnamoorthy, R., Kamala, K., Soubache, I. D., Karthik, M. V., & Begum, M. A. (2022) · Wiley eBooks · doi:10.1002/9781119785569.ch3 · Cited by 18 · see citing works
Machine learning applications in gynecological cancer: A critical review
Fiste, O., Liontos, M., Zagouri, F., STAMATAKOS, G. S., & Dimopoulos, M. A. (2022) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2022.103808 · Cited by 16 · see citing works
Künstliche Intelligenz in der Hochschulbildung
(2023) · transcript Verlag eBooks · doi:10.1515/9783839457696 · Cited by 16 · see citing works
“There’s No Data Like More Data”
Li, X. (2023) · Osiris · doi:10.1086/725132 · Cited by 16 · see citing works
Concrete strength and durability prediction through deep learning and artificial neural networks
Hosseinzadeh, M., Samadvand, H., Hosseinzadeh, A., Mousavi, S. S., & Dehestani, M. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1124-9 · Cited by 16 · see citing works
Abdelsattar, D. M., Owais, M., Fahmy, M. F. M., Osman, R., & Nafadi, M. K. (2025) · International Journal of Concrete Structures and Materials · doi:10.1186/s40069-025-00815-y · Cited by 15 · see citing works
Oyebisi, S. O., Owamah, H. I., & Omeje, M. (2023) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2023.100629 · Cited by 15 · see citing works
Computational intelligence based design of biomaterials
Vinoth, A., & Datta, S. (2022) · Computer Methods in Materials Science. · doi:10.7494/cmms.2022.4.0799 · Cited by 15 · see citing works
Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
Dou, X., & Yang, Y. (2017) · Forests · doi:10.3390/f8120498 · Cited by 15 · see citing works
Beccaria, M., Franchina, F. A., Nasir, M., Mellors, T. R., Hill, J. E., & Purcaro, G. (2021) · Molecules · doi:10.3390/molecules26154600 · Cited by 15 · see citing works
Gupta, M. M., & Gupta, A. (2021) · Symbiosis · doi:10.1007/s13199-021-00778-0 · Cited by 14 · see citing works
25 Years of Knowledge Acquisition
Motta, E. (2012) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2012.11.002 · Cited by 14 · see citing works
AI Evaluation: past, present and futurePreprint
Hernández‐Orallo, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.6908 · Cited by 18 · see citing works
Advancements in AI for Computational Biology and Bioinformatics: A Comprehensive Review
Nalina, V., Prabhu, D., Sahayarayan, J. J., & Vidhyavathi, R. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4690-8_6 · Cited by 17 · see citing works
Ali, O., Murray, P. A., Al-Ahmad, A., Jeon, I., & Dwivedi, Y. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100762 · Cited by 9 · see citing works
New and emerging technologies in paleontology and paleobiology: A horizon scanning review
Abdelhady, A. A., Seuß, B., Jain, S., Abdel-Raheem, K. H. M., Elsheikh, A., Ahmed, M. S., Elewa, A. M. T., & Hussain, A. M. (2023) · Journal of African Earth Sciences · doi:10.1016/j.jafrearsci.2023.105155 · Cited by 15 · see citing works
A new paradigm of reservoir computing exploiting hydrodynamics
Marcucci, G., Caramazza, P., & Shrivastava, S. (2023) · Physics of Fluids · doi:10.1063/5.0157919 · Cited by 13 · see citing works
Ma, Z., Bao, H., Zhang, S., Xian, M., & Mack, A. L. (2022) · doi:10.2172/1847070 · Cited by 13 · see citing works
Machine Learning in Computer Aided Engineering
Montáns, F. J., Cueto, E., & Bathe, K. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-36644-4_1 · Cited by 13 · see citing works
Fundamentals of Machine Learning
Vinoth, A., & Datta, S. (2021) · Management and industrial engineering · doi:10.1007/978-3-030-75847-9_1 · Cited by 12 · see citing works
Deep Learning Applications in Medical Imaging
Janani.V, Saranya, N. N., & Inbamani, A. (2020) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-5071-7.ch007 · Cited by 11 · see citing works
Special Issue on Autonomous Learning
Hammer, B., & Toussaint, M. (2015) · Künstliche Intell. · doi:10.1007/s13218-015-0392-x · Cited by 11 · see citing works
Turning biases into hypotheses through method: A logic of scientific discovery for machine learning
Enni, S. A., & Herrie, M. B. (2021) · Big Data & Society · doi:10.1177/20539517211020775 · Cited by 10 · see citing works
Artificial Intelligence and Machine Learning in Bioinformatics
Alam, S., Israr, J., & Kumar, A. (2024) · Springer eBooks · doi:10.1007/978-981-99-8401-5_16 · Cited by 10 · see citing works
Marine mammal morphometrics: 3D modeling and estimation validation
Zhang, C., Zhou, H., Christiansen, F., Hao, Y., Wang, K., Kou, Z., Chen, R., Min, J., Davis, R. W., & Wang, D. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1105629 · Cited by 9 · see citing works
Release modeling of nanoencapsulated food ingredients by artificial intelligence algorithms
Djuriš, J., Vidović, B. B., & Ibric, S. R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815665-0.00009-6 · Cited by 9 · see citing works
Kline, A. S., Kline, T. J. B., Abad, Z. S. H., & Lee, J. (2020) · Journal of Medical Internet Research · doi:10.2196/20268 · Cited by 9 · see citing works
AL-Shami, E. S., Owais, M., Raheem, S. E. A., Mohamed, W. A. E.-W., & Fahmy, M. F. M. (2025) · Innovative Infrastructure Solutions · doi:10.1007/s41062-025-02050-6 · Cited by 8 · see citing works
Bahij, M., Labbadi, M., Cherkaoui, M., Chatri, C., Elkhatiri, A., & Elouerghi, A. (2021) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect53699.2021.9668559 · Cited by 8 · see citing works
Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry
Fricke, S., Salgado, M., Menezes, T. R., Santos, K. M. C., Gallagher, N. B., Song, A., Wang, J., Engler, K. E., Wang, Y., Mao, H., et al. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202316664 · Cited by 8 · see citing works
Digital Health Technology Implementation: Is It Effective in a Healthy Healthcare Perspective?
Stein, A. T., Ben, Â. J., Pachito, D. V., Cazella, S. C., van Dongen, J. M., & Bosmans, J. E. (2020) · Springer eBooks · doi:10.1007/978-3-030-59467-1_9 · Cited by 8 · see citing works
Ethical Challenges and Frameworks in AI-Driven Software Development and Testing
Donvir, A., & Sharma, G. (2025) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc62904.2025.10903892 · Cited by 6 · see citing works
Blum, S., Klaproth, O. W., & Rußwinkel, N. (2022) · Topics in Cognitive Science · doi:10.1111/tops.12594 · Cited by 6 · see citing works
Eder, E. B., Almonacid, J. S., Delrieux, C. A., & Lewis, M. (2022) · Marine Mammal Science · doi:10.1111/mms.12910 · Cited by 6 · see citing works
Hernes, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-662-58611-2_1 · Cited by 6 · see citing works
Using Machine Learning to improve the performance of Public Enterprises
Saxena, N. C. (2023) · Public Enterprise Half-Yearly Journal · doi:10.21571/pehyj.2023.2701.03 · Cited by 5 · see citing works
Background of machine learning
Qi, C., Yilmaz, E., & Chen, Q. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15524-6.00015-7 · Cited by 5 · see citing works
The Impact of Big Data on Climate Change
Shrivastava, A. C., & Umar, M. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10099659 · Cited by 5 · see citing works
Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems
Yu, H., Zhu, Y., Zhu, L. Q., Lin, X., & Wan, Q. C. (2022) · Frontiers in Nanotechnology · doi:10.3389/fnano.2022.954165 · Cited by 5 · see citing works
Comparison of the Usability of Apple M1 Processors for Various Machine Learning Tasks
Kasperek, D., Podpora, M., & Kawala‐Sterniuk, A. (2022) · Sensors · doi:10.3390/s22208005 · Cited by 5 · see citing works
Inferring Feature Relevances From Metric Learning
Schulz, A., Mokbel, B., Biehl, M. L., & Hammer, B. (2015) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2015.225 · Cited by 5 · see citing works
DIREITOS AUTORAIS E MÚSICA: TECNOLOGIA, DIREITO E REGULAÇÃO
Ribeiro, M. C. P., Freitas, C. O. D. A., & Neves, R. C. (2018) · Revista Brasileira de Políticas Públicas · doi:10.5102/rbpp.v7i3.4799 · Cited by 4 · see citing works
Digital Forensics and the Big Data Deluge — Some Concerns Based on Ramsey Theory
Olivier, M. S. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-56223-6_1 · Cited by 4 · see citing works
Silkhi, H., Bakkas, B., & Housni, K. (2024) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3659677.3659825 · Cited by 4 · see citing works
Using Multi-agent Systems to Pursue Autonomy with Automated Components
Tweedale, J. W. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.09.226 · Cited by 4 · see citing works
Pattern recognition between science and engineering: A red herring?
Pelillo, M., Scantamburlo, T., & Schiaffonati, V. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.06.030 · Cited by 4 · see citing works
Menthe, L., Goldfeld, D. A., Tingstad, A., Lingel, S., Geist, E., Brunk, D., Wicker, A., Lovell, S., Gintautas, B., Stickells, A., et al. (2021) · RAND Corporation eBooks · doi:10.7249/rra341-2 · Cited by 4 · see citing works
Deceiving neural source code classifiers
Ferretti, C., & Saletta, M. (2021) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3449726.3463222 · Cited by 4 · see citing works
Avuçlu, E. (2023) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2023.2196984 · Cited by 4 · see citing works
- Authors
- Langley, P.
- Venue
- Machine Learning
- Published
- 2011-02-18
- Volume
- 82
- Issue
- 3
- Pages
- 275-279
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2038507439
- Cited by
- 174
- References
- 11
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 14 · 2023: 21 · 2022: 19 · 2021: 29 · 2020: 17 · 2019: 15 · 2018: 9 · 2017: 8 · 2016: 3 · 2015: 8 · 2014: 5 · 2013: 5 · 2012: 6
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- ResearchSpace (University of Auckland) (submittedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Physical Sciences › Computer Science › Computer Science Applications › History of Computing Technologies
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Historical Studies and Socio-cultural Analysis
- Keywords
- history of machine learning, journal founding, computational learning theory, journal editorial history
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
