The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Machine Learning for Sequential Data: A Review
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Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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
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 23,522
(1992) · Cited by 23,431
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 20,825
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
Cited by
Online learning: A comprehensive survey
Hoi, S. C. H., Sahoo, D., Lu, J., & Zhao, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.112 · Cited by 746
Empirical prediction models for adaptive resource provisioning in the cloud
Islam, S., Keung, J., Lee, K., & Liu, A. (2012) · Future Generation Computer Systems · doi:10.1016/j.future.2011.05.027 · Cited by 631
Twenty Years of Mixture of Experts
Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2200299 · Cited by 606
Conditional random fields for activity recognition
Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357
A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories
Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02332-5 · Cited by 349
Metadata
- Authors
- Dietterich, T. G.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 15-30
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1529355025
- Cited by
- 666
- References
- 26
- FWCI
- 9.75
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Machine Learning for Sequential Data: A Review. https://app.cochat.ai/papers/10.1007/3-540-70659-3_2
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- Dietterich, T. G. (2002). Machine Learning for Sequential Data: A Review. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-70659-3_2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
