The DOI matches its Crossref record.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Open accessAbstract
This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
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In the citing paper
“The principles of SVM were introduced in Chou and Cai [60,61], and more details are provided in Cristianini [62].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [62]
In the citing paper
“Machine learning models such as support vector machines20, k-Nearest Neighbors21, Naïve Bayes22, Random Forest23 and many other methods24, have long been utilized for drug discovery.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [20]
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Items 101–107 of 107
Ghidoni, S., Antonello, M., Nanni, L., & Menegatti, E. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.07.020 · Cited by 18
Detecting online child grooming conversation
Gunawan, F. E., Ashianti, L., Candra, S., & Soewito, B. (2016) · 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS) · doi:10.1109/kicss.2016.7951413 · Cited by 17
Haq, E. U., Huarong, X., Xuhui, C., Wanqing, Z., Jianping, F., & Abid, F. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08167-y · Cited by 12
Almalawi, A. (2025) · Sensors · doi:10.3390/s25072235 · Cited by 10
Real-Time Steel Inspection System Based on Support Vector Machine and Multiple Kernel Learning
Chen, Y., Chen, L., Liu, X., Ding, S., & Zhang, H. (2011) · Advances in Intelligent and Soft Computing · doi:10.1007/978-3-642-25658-5_21 · Cited by 9
Automatic placental maturity grading via hybrid learning
Lei, B., Tan, E.-L., Chen, S., Li, W., Ni, D., Yao, Y., & Wang, T. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.10.033 · Cited by 5
Porębski, A. (2023) · Research Handbook on Law and Technology · doi:10.4337/9781803921327.00037 · Cited by 4
Metadata
- Authors
- Cristianini, N., & Shawe-Taylor, J.
- Affiliations
- University of Bristol (GB)
- Royal Holloway University of London (GB)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2000-03-23
- OpenAlex
- W1563088657
- Cited by
- 13,855
- References
- 0
- Citations by year
- 2026: 143 · 2025: 261 · 2024: 305 · 2023: 364 · 2022: 429 · 2021: 577 · 2020: 589 · 2019: 614 · 2018: 618 · 2017: 618 · 2016: 651 · 2015: 741 · 2014: 762 · 2013: 726 · 2012: 763
- FWCI
- 56.63
- Type
- book
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Virtual Defense Library (Ministerio de Defensa) (submittedVersion)
- International Linear Collider (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- support vector machine, kernel methods, statistical learning theory, text classification, handwritten character recognition, image classification, machine learning, data mining
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