An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cristianini, Nello; Shawe-Taylor, John

doi:10.1017/cbo9780511801389

Cited by 13,855Machine Learning and AlgorithmsMachine Learning and ELMStochastic Gradient Optimization Techniques

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

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Cristianini, Nello, Shawe-Taylor, John·2000·Cambridge University Press eBooks

DOI 10.1017/cbo9780511801389

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Abstract

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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The principles of SVM were introduced in Chou and Cai, and more details are provided in Cristianini.
“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]
Machine learning models such as support vector machines, k-Nearest Neighbors, Naïve Bayes, Random Forest and many other methods have long been utilized for drug discovery.
“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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Authors: Cristianini, N., & Shawe-Taylor, J.
Affiliations: University of Bristol (GB)
Royal Holloway University of London (GB)
ORCID: Cristianini, Nello
Shawe-Taylor, John
Venue: Cambridge University Press eBooks
Publisher: Cambridge University Press
Published: 2000-03-23
DOI: 10.1017/cbo9780511801389
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: cc-by-nc-nd
www.cambridge.org/core/terms (unspecified, 4730 d)
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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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

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