Kernel Methods for Pattern Analysis

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...

Celý popis

Uložené v:
Podrobná bibliografia
Hlavní autori: Shawe-Taylor, John, Cristianini, Nello
Médium: E-kniha Kniha
Jazyk:English
Vydavateľské údaje: Cambridge Cambridge University Press 28.06.2004
Vydanie:1
Predmet:
Algorithms
Data processing
Kernel functions
Machine learning
Pattern perception
Pattern perception > Data processing
ISBN:9780521813976, 0521813972
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:Includes bibliographical references (p. 450-459) and index
ISBN:9780521813976
0521813972
DOI:10.1017/CBO9780511809682