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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.
Sample Chapter(s)
Chapter 1: Why Kernels for Structured Data? (228 KB)
Contents:
- Why Kernels for Structured Data?
- Kernel Methods in a Nutshell
- Kernell Design
- Basic Term Kernels
- Graph Kernels
Readership: Researchers, academics, graduate and advanced undergraduates in machine learning and artificial intelligence.
