Kernels For Structured Data
Book Details
Format
Hardback or Cased Book
ISBN-10
9812814558
ISBN-13
9789812814555
Publisher
World Scientific Publishing Co Pte Ltd
Imprint
World Scientific Publishing Co Pte Ltd
Country of Manufacture
SG
Country of Publication
GB
Publication Date
Sep 2nd, 2008
Print length
216 Pages
Weight
514 grams
Dimensions
23.50 x 16.00 x 1.90 cms
Product Classification:
Machine learning
Ksh 14,750.00
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Answers the question of how kernel methods can be applied to structured data. This book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is suitable for readers who seek an entry point into the field as well as experienced researchers.
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.
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