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Support Vector Machines for Pattern Classification
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Citations
Cited by
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks
Jiang, W., & Yin, Z. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2806333 · Cited by 676
Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
Al-Nasheri, A., Muhammad, G., Alsulaiman, M., Ali, Z., Malki, K. H., Mesallam, T. A., & Farahat Ibrahim, M. (2018) · IEEE Access · doi:10.1109/access.2017.2696056 · Cited by 174
Machine learning for inferring animal behavior from location and movement data
Wang, G. (2019) · Ecological Informatics · doi:10.1016/j.ecoinf.2018.12.002 · Cited by 154
Methodologies in power systems fault detection and diagnosis
Aleem, S. A., Shahid, N., & Naqvi, I. H. (2015) · Energy Systems · doi:10.1007/s12667-014-0129-1 · Cited by 83
Exploring ELM-based spatial–spectral classification of hyperspectral images
Heras, D. B., Argüello, F., & Quesada-Barriuso, P. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.869633 · Cited by 54
Fast data selection for SVM training using ensemble margin
Guo, L., & Boukir, S. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.08.003 · Cited by 49
Age detection through keystroke dynamics from user authentication failures
Tsimperidis, I., Rostami, S., & Katos, V. (2017) · International Journal of Digital Crime and Forensics · doi:10.4018/ijdcf.2017010101 · Cited by 26
Metadata
- Venue
- Advances in Pattern Recognition
- Publisher
- Springer-Verlag
- Published
- 2005
- OpenAlex
- W1503196035
- Cited by
- 852
- References
- 0
- FWCI
- 15.62
- Type
- book
- 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.
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.
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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.
