Cited by 174Publishing and Scholarly CommunicationHistory of Computing TechnologiesHistorical Studies and Socio-cultural Analysis

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

The changing science of machine learning

Open access

Langley, Pat·2011·Machine Learning

DOI 10.1007/s10994-011-5242-y

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.1007/s10994-011-5242-y

Cites

11 of 11 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Untitled work
(2013) · Springer eBooks · Cited by 2,625
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
Explanation-Based Learning: An Alternative View
DEJONG, G. F., & Mooney, R. J. (1986) · Machine Learning · doi:10.1023/a:1022898111663 · Cited by 839
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
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
Why Should Machines Learn?
Simon, H. A. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_2 · Cited by 267
Machine learning as an experimental science
Langley, P. (1988) · Machine Learning · doi:10.1007/bf00115008 · Cited by 187
Self-citation
Automated 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
Toward a Unified Science of Machine Learning
Langley, P. (1989) · Machine Learning · doi:10.1023/a:1022689616458 · Cited by 39
Self-citation
Research Papers in Machine Learning
Langley, P. (1987) · Machine Learning · doi:10.1023/a:1022603230145 · Cited by 12
Self-citation

Show all 11 references

Sorted by citations

Items 101–170 of 170

Regulating the Tyrell Corporation: the Emergence of Novel Beings
Lawrence, D. R., & Morley, S. K. (2021) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180120000973 · Cited by 4
Why does machine learning work really well in many engineering problems?
Naser, M. Z. (2025) · Machine learning for computational science and engineering · doi:10.1007/s44379-025-00032-0 · Cited by 4
Study on Modelling Human Behavior in Cooperative Games
Takko, T. (2019) · Cited by 3
Enhancing the Degree of Autonomy by Creating Automated Components within a Multi-Agent System Framework
Tweedale, J. W. (2015) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-13545-8_15 · Cited by 3
La vida conectiva. Las redes digitales como espejos sociotécnicos de Iberoamérica
Sociedade, C. D. E. D. C. E., Costa, P. R., Capoano, E., Ibáñez, D. B., & del Rosario, U. (2021) · Chasqui Revista Latinoamericana de Comunicación · doi:10.16921/chasqui.v1i147.4618 · Cited by 3
Machine Learning for Building Energy and Indoor Environment: A PerspectivePreprint
Liu, Z., Wu, D., Wei, H., & Cao, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.00779 · Cited by 4
Freedom of Scientific Research
Spigno, I. (2018) · Oxford University Press eBooks · doi:10.1093/law-mpeccol/e169.013.169 · Cited by 3
The Multiplier Effect of Applied Machine Learning Technology in Modern Healthcare
Bandyopadhyay, A. (2020) · International Journal of Information Science and Computing · doi:10.30954/2348-7437.1.2020.4 · Cited by 2
Propuesta de una herramienta de análisis del clima a través de predictivas de datos
Mateus, C. A. G. (2021) · GRAINE Boletín de Investigaciones · doi:10.52408/gbdivol2iss2pp13-20 · Cited by 2
Computational Statistics and Predictive Analysis in Machine Learning
(2016) · International Journal of Science and Research (IJSR) · doi:10.21275/v5i1.nov152818 · Cited by 2
Das begriffliche Ökosystem von AAS
Seebacher, U. (2021) · Essentials · doi:10.1007/978-3-658-34682-9_2 · Cited by 2
Mineral Informatics: Origins
Prabhu, A., Morrison, S. M., & Hazen, R. M. (2023) · Springer mineralogy · doi:10.1007/978-3-031-28805-0_3 · Cited by 2
Chatbots und mediengestützte Konversation
Spierling, U., & Luderschmidt, J. (2018) · Springer eBooks · doi:10.1007/978-3-658-23297-9_22 · Cited by 3
Advanced bioscience and AI: debugging the future of life
Lawrence, D. R. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180069 · Cited by 2
Data-Driven Prediction of CRISPR-Based Transcription Regulation for Programmable Control of Metabolic FluxPreprint
Sheng, J., Guo, W., Ash, C., Freitas, B. T., Paoletti, M., & Feng, X. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/119396 · Cited by 4
IMPROVING PATH PLANNING PERFORMANCE BY MONITORING HUMAN DECISIONS
Cheng, C. C. (2012) · OpenSIUC (Southern Illinois University Carbondale) · Cited by 2
Artificial Intelligence System for Predicting Cardiovascular Diseases Using IoT Devices and Virtual Instrumentation
Modran, H. A., Ursuțiu, D., Samoilă, C., & Chamunorwa, T. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82529-4_28 · Cited by 1
Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment
Piir, G., Sild, S., Tcheremenskaia, O., Consiglio, E. D., Henriksson, J., Gajewicz, A., Mombelli, E., Roncaglioni, A., & Maran, U. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01184-8 · Cited by 1
Continuous Auditing with Machine Learning: Early Flagging of Risky Billing Documents from SAP
Duhova, R., Duhov, A., Lazarova, M. K., & Georgieva, P. (2025) · International Scientific Conference on Computer Science (COMSCI) · doi:10.1109/comsci67172.2025.11225259 · Cited by 1
Optimization of Dataset Based on Hybrid Algorithms
Flayeh, A. K., Douik, A., & Thajeel, S. A. (2023) · Al-Sadiq International Conference on Communication and Information Technology (AICCIT) · doi:10.1109/aiccit57614.2023.10218233 · Cited by 1
Enhancing the Dgree of Autonomy on a ‘Tier 1’ Unmanned Aerial Vehicle Using a Visual Landing Framework
Tweedale, J. W., & Gonano, D. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.08.190 · Cited by 1
Semantics in Deep Neural-Network Computing
Sun, X., Luo, X., Liu, J., Jiang, X., & Zhang, J. (2015) · International Conference on Semantics, Knowledge and Grid (SKG) · doi:10.1109/skg.2015.42 · Cited by 1
Machine Learning for Database Management Systems
Sai Tanishq, N. (2020) · International Journal Of Engineering And Computer Science · doi:10.18535/ijecs/v9i08.4520 · Cited by 1
Das Predictive Intelligence Ökosystem
Seebacher, U. (2021) · Springer eBooks · doi:10.1007/978-3-662-62776-1_3 · Cited by 1
The freedom of scientific research
(2018) · Manchester University Press eBooks · doi:10.7765/9781526127686 · Cited by 1
Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys
Jeong, J., Jang, H., Caesary, D., Joung, I. S., Cho, A., Yoon, D., & Nam, M. J. (2022) · Journal of the Korean Society of Mineral and Energy Resources Engineers · doi:10.32390/ksmer.2022.59.4.379 · Cited by 1
A Machine Learning Mechanism for Improved Energy Consumption in 5G Small Cells
Allayiotis, F., Christos, B., & Kokkinos, V. (2022) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc55528.2022.9913060 · Cited by 1
Evaluating visually grounded language capabilities using microworlds
Kuhnle, A. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.49177 · Cited by 1
References
Kuncheva, L. I. (2014) · Wiley eBooks · doi:10.1002/9781118914564.refs · Cited by 1
A note about the generalisation of the C-testsPreprint
Hernández‐Orallo, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.8529
Enhancing sentiment extraction from text by means of arguments
Carstens, L., & Toni, F. (2013) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2502069.2502073
Editorial — Special Issue on Autonomous Learning
Roy, A., Apolloni, B., PERLOVSKY, L. I., & Minai, A. A. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.03.008
Automatic Semantic Characterization of Drum Sounds
Pinto, A. S. (2015)
17 th International Conference in Knowledge Based and Intelligent Information and Engineering Systems - KES2013 Using Multi-Agent Systems to Pursue Autonomy with Automated Components
Tweedale, J. W., & Lakes, M. (2013)
Supervised machine learning for document analysis and prediction
Ghany, K. K. A., & Ayeldeen, H. (2015) · World Conference on Complex Systems · doi:10.1109/icocs.2015.7483232
Estimage: A mobile application in agriculture for image to object count estimation
Chen, Y. (2017) · Proceedings of the Nova Scotian Institute of Science · doi:10.15273/pnsis.v49i1.6983
A measure of visuospatial reasoning skills: Painting the big picture
Michelson, J., Sanyal, D., Ainooson, J., & Kunda, M. (2020)
Brief Review of Machine Learning in Neurosurgery
Roka, Y. B. (2018) · Nepal Journal of Neuroscience · doi:10.3126/njn.v15i3.23268
The Predictive Intelligence Ecosystem
Seebacher, U. (2021) · Future of business and finance · doi:10.1007/978-3-030-69403-6_3
The Role of Climate Datasets in Understanding Climate ExtremesPreprint
Eresanya, E. O., Orimoloye, I. R., Durowoju, O. S., Daramola, M. T., Olowe, J. P., Folorunsho, A. H., & Olorunsaye, O. (2021) · Preprints.org · doi:10.20944/preprints202105.0139.v1
Einsatz Künstlicher Intelligenz in Einkaufsverhandlungen – Status quo und Anwendungsfälle
Voeth, M., Oryl, M., & Bronnert, N. F. (2021) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-34326-2_7
Das begriffliche Ökosystem von DGM
Seebacher, U. (2021) · Essentials · doi:10.1007/978-3-658-34585-3_2
Deep learning in cardiovascular imaging
Zeleznik, R. (2021) · doi:10.26481/dis.20210916rz
The freedom of scientific research
(2020) · Manchester University Press eBooks · doi:10.7765/9781526146472
Robotic intelligence
Lawrence, D. (2020) · Manchester University Press eBooks · doi:10.7765/9781526146472.00018
Bibliography
Dey, S., Bhattacharyya, S., & Maulik, U. (2019) · Wiley eBooks · doi:10.1002/9781119488767.biblio
Purpose-driven Content Network Transmission Protocol Crossing DIKW Modals
Lei, Y., & Duan, Y. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00350
General Learning
Richter, M. M., Paul, S., Këpuska, V., & Silaghi, M. C. (2022) · Springer eBooks · doi:10.1007/978-3-319-45372-9_8
Signal Processing Background
Richter, M. M., Paul, S., Këpuska, V., & Silaghi, M. C. (2022) · Springer eBooks · doi:10.1007/978-3-319-45372-9_2
Künstliche Intelligenz in der Bildgebung
Schneider, M. A. (2023) · Uro-News · doi:10.1007/s00092-023-5663-5
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 · doi:10.1002/ange.202316664
Defining Relational Learning Beyond Individual Learning Within Organizations
Schwengber, J. G. (2024) · Relational economics and organization governance · doi:10.1007/978-3-031-52015-0_4
Will Artificial Intelligence Replace Knowledge Centers? Assessment of the Situation
Alav, O. (2024) · Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD) · doi:10.30785/mbud.1441134
L'intelligence, des origines naturelles aux frontières artificielles - Intelligence humaine vs. Intelligence artificielle
Sfetcu, N. (2024) · doi:10.58679/mm81419
Application of Neural Networks in Data Analysis
Liu, F. (2024) · Eötvös Loránd Tudományegyetem · doi:10.15476/elte.2023.100
Additional file 6 of “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health
Morgenstern, J., Rosella, L. C., Daley, M. J., Goel, V., Schünemann, H. J., & Piggott, T. (2021) · Figshare · doi:10.6084/m9.figshare.13534369
Additional file 3 of “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health
Morgenstern, J., Rosella, L. C., Daley, M. J., Goel, V., Schünemann, H. J., & Piggott, T. (2021) · Figshare · doi:10.6084/m9.figshare.13534360
Additional file 1 of “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health
Morgenstern, J., Rosella, L. C., Daley, M. J., Goel, V., Schünemann, H. J., & Piggott, T. (2021) · Figshare · doi:10.6084/m9.figshare.13534354
Additional file 2 of “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health
Morgenstern, J., Rosella, L. C., Daley, M. J., Goel, V., Schünemann, H. J., & Piggott, T. (2021) · Figshare · doi:10.6084/m9.figshare.13534357
A Scheduling and Partitioning Model for Stencil-based Applications on Many-Core Devices
Papin, J.-C. (2016) · doi:10.70675/6c3efaeez4654z453cza65fzadd6d116000d
Detection and quantiﬁcation of events in stochastic systems
Bazille, H. (2019) · doi:10.70675/0ae8739dz3d1bz4281z8142z7634475efe73
Reduced shape-space : approach to material characterization instrumented indentation test case
Meng, L. (2017) · theses.fr (ABES) · doi:10.70675/89c6f22cz1142z4ca6zadbfz47c16e905673
Méthodes de vision par ordinateur et d'apprentissage profond pour la localisation, le suivi et l'analyse de structure de plantes : application au désherbage de précision
Lac, L. (2022) · theses.fr (ABES) · doi:10.70675/dbc1d302za342z4637z8e82z6a7fbde4a863
Développement d'une plateforme pour le phénotypage à haut-débit des cellules souches cancéreuses par intelligence artificielle
Chambost, A. J. (2022) · theses.fr (ABES) · doi:10.70675/85bd0efcz09c5z484eza756z7d52a9d4329a
Designing smart home services using machine learning and knowledge-based approaches
Qiu, M. (2023) · theses.fr (ABES) · doi:10.70675/91d484b0z239bz4bcbz939cze37dcafb860a
Development of an XGBoost-based regression model and experimental validation for predicting the compressive strength of fully recycled aggregate concrete
Sun, M., Xi, X., Zheng, Y., Wang, L., & Wang, K. (2026) · Structures · doi:10.1016/j.istruc.2026.111816
Impact of Artificial Intelligence on human behavior and psychology
Nandi, K., & Nandi, K. (2026) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2026.19.1.0859
Civil engineering in the age of artificial intelligence: state of the art of breakthrough, innovation, and intelligent design
Saeed, N. M., & Hassan, H. Z. (2026) · Architecture Structures and Construction · doi:10.1007/s44150-026-00222-1
AI_MDD Approaches for Generating Adaptive and Intelligent UIs: A Systematic Literature Review
Aljabri, A., Hijazi, H., Salamin, S., & Khemaja, M. (2026) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2026.p1620
Reconceptualizing learning sciences in the era of artificial intelligence: Toward hybrid intelligence and adaptive learning systems
Malik, A., & Bashir, I. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107977

Authors: Langley, P.
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 2011-02-18
Volume: 82
Issue: 3
Pages: 275-279
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1007/s10994-011-5242-y
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 › Library and Information Sciences › 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

The changing science of machine learning

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks