Kernels for structured data
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....
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| Hlavní autor: | |
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
New Jersey
World Scientific Publishing Co. Pte. Ltd
2008
World Scientific World Scientific Publishing Company WORLD SCIENTIFIC World Scientific Publishing |
| Vydání: | 1 |
| Edice: | Series in machine perception and artificial intelligence |
| Témata: | |
| ISBN: | 9812814558, 9789812814555, 9789812814562, 9812814566 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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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| Bibliografie: | Includes bibliographical references (p. 179-190) and index |
| ISBN: | 9812814558 9789812814555 9789812814562 9812814566 |
| DOI: | 10.1142/6855 |