Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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DOI：
10.1109/ictai.2003.10009
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
J. Shawe-Taylor;N. Cristianini
中科院分区：
文献类型：
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作者：
J. Shawe-Taylor;N. Cristianini
核方法为模式发现提供了一个强大而统一的框架，激励算法可以作用于一般类型的数据（例如字符串，向量或文本）并寻找一般类型的关系（例如排名，分类，回归，聚类）。应用领域从神经网络和模式识别到机器学习和数据挖掘。这本书，从讲座和教程开发，履行两个主要角色：首先，它为从业者提供了一个大型的算法，内核和解决方案的工具包，准备用于生物信息学，文本分析，图像分析等领域的标准模式发现问题。其次，它为学生和研究人员提供了一个简单的介绍，以不断增长的基于内核的模式分析领域，演示了如何手工制作一个新的特定应用程序的算法或内核，并涵盖了所有必要的概念和数学工具。
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.