The DOI matches its Crossref record; 310 of the 310 references it cites were found in a registry or an index.
Series in Machine Perception and Artificial Intelligence
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Measuring the efficiency of decision making units
Charnes, A., Cooper, W. W., & Rhodes, E. (1978) · European Journal of Operational Research · doi:10.1016/0377-2217(78)90138-8 · Cited by 29,767
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
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
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 20,598
(1996) · Cited by 20,179
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
Cited by
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559
Islam, M. S., Islam, M. T., Sarker, S., Jame, H. A., Nishat, S. S., Jani, M. R., Rauf, A., Ahsan, S., Shorowordi, K. M., Efstathiadis, H., et al. (2022) · ACS Omega · doi:10.1021/acsomega.2c01076 · Cited by 76
Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning
Rivail, A., Schmidt-Erfurth, U., Vogl, W.-D., Waldstein, S. M., Riedl, S., Grechenig, C., Wu, Z., & Bogunovic, H. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32281-6_5 · Cited by 25
Metadata
- Venue
- Series in machine perception and artificial intelligence
- Publisher
- WORLD SCIENTIFIC
- Published
- 2012-07-18
- ISSN
- 1793-0839 (ISSN-L)
- OpenAlex
- W2309460698
- Cited by
- 365
- References
- 310
- Type
- paratext
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Series in Machine Perception and Artificial Intelligence. https://app.cochat.ai/papers/10.1142/smpai
- The paper
- Series in Machine Perception and Artificial Intelligence. (2012). Series in machine perception and artificial intelligence. https://doi.org/10.1142/smpai
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.
