KERNELS FOR STRUCTURED DATA

结构化数据的核心

计算机科学技术基础学科

原   价:
955.00
售   价:
716.00
发货周期:预计3-5周发货
作      者
出  版 社
出版时间
2008年09月02日
装      帧
精装
ISBN
9789812814555
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页      码
216
语      种
英文
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图书简介
This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers. Key Features • Introduces the most recently developed kernel methods • Discusses fundamental kernel design principles • Surveys the most important kernels for structured data • Contains an in-depth discussion of kernels for graphs
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