An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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DOI：
10.1017/cbo9780511801389.013
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发表时间：
2000-03
期刊：
影响因子：
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通讯作者：
N. Cristianini;J. Shawe-Taylor
中科院分区：
文献类型：
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作者：
N. Cristianini;J. Shawe-Taylor
来自发行者：这是基于统计学习理论的最新进展，这是支持向量机（SVM）的第一个全面介绍。 SVM在现实世界中提供最先进的性能，例如文本分类，手写字符识别，图像分类，BioSequence Analysis等，现在已被确定为机器学习和数据的标准工具之一矿业。学生会发现这本书既刺激又易于获取，而从业者将通过对理论及其应用的良好掌握所需的材料进行顺利进行。这些概念逐渐在可访问和独立的阶段逐渐引入，而演讲则是严格而彻底的。指示相关的文献和包含软件的网站确保它构成了进一步研究的理想起点。同样，本书及其相关的网站将指导从业人员更新文献，新应用程序和在线软件。
From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new 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.