The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
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
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Schölkopf, B., Smola, A., & Müller, K.-R. (1998) · Neural Computation · doi:10.1162/089976698300017467 · Cited by 8,084
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
Least Squares Support Vector Machines
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002) · WORLD SCIENTIFIC eBooks · doi:10.1142/5089 · Cited by 3,642
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308
Support Vector Machines for Classification and Regression
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Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727
Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
Abbas, F., Afzaal, H., Farooque, A. A., & Tang, S. (2020) · Agronomy · doi:10.3390/agronomy10071046 · Cited by 259
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Kristinsson, S., Zhang, W., Rorden, C., Newman‐Norlund, R., Basilakos, A., Bonilha, L., Yourganov, G., Xiao, F., Hillis, A., & Fridriksson, J. (2021) · Human Brain Mapping · doi:10.1002/hbm.25321 · Cited by 64
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“Choosing appropriate hyperparameters, especially kernel functions, is a difficult task [49].”
BackgroundLee, S. I., Park, E., Huang, A., Mortazavi, B., Hayward Garst, J., Jahanforouz, N., Espinal, M., Siero, T., Pollack, S., Afridi, M., et al. (2016) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.02.004 · Cited by 41
Metadata
- Authors
- Üstün, B., Melssen, W. J., & Buydens, L. M. C.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2006-03
- Volume
- 81
- Issue
- 1
- Pages
- 29-40
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2126819771
- Cited by
- 305
- References
- 22
- FWCI
- 2.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Üstün, B., Melssen, W. J., & Buydens, L. M. C. (2006). Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2005.09.003
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.
