Chemometrics pattern recognition methods

Yun Xu

ISBN 10: 3659262099 / ISBN 13: 9783659262098

Verlag: LAP LAMBERT Academic Publishing Okt 2012, 2012

Beschreibung

This item is printed on demand - Print on Demand Titel. Neuware -During my Ph.D study I had been challenged by many pattern recognition tasks which coming from very different disciplines. Each task had it's unique problems. It took great efforts to find a suitable way to solve each of the problem. In return, this thesis has covered a very broad spectrum of Chemometrics pattern recognition techniques, from well known and commonly used models such as PCA, PLS-DA to state of the art models such as kernel learning and support vector machines. There is also a good coverage of the methods which are much less known to the community but could be very useful methods such as principal coordiante analysis, featureless pattern recognitions and multivariate pattern comparison. The usefulness of these methods were demonstrated through several real-life applications including single nucleotide polymorphism analysis, DNA microarray analysis and a few human and mice metabolomic profiling studies. The comprehensive coverage of chemometrics pattern recognition techniues combined with several applications could be very useful to those who work in a similar discipline.Books on Demand GmbH, Überseering 33, 22297 Hamburg 268 pp. Englisch.

Bestandsnummer des Verkäufers 9783659262098

Bibliografische Details

• Titel: Chemometrics pattern recognition methods
• Verlag: LAP LAMBERT Academic Publishing Okt 2012
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 3659262099
• ISBN-13: 9783659262098
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

During my Ph.D study I had been challenged by many pattern recognition tasks which coming from very different disciplines. Each task had it's unique problems. It took great efforts to find a suitable way to solve each of the problem. In return, this thesis has covered a very broad spectrum of Chemometrics pattern recognition techniques, from well known and commonly used models such as PCA, PLS-DA to state of the art models such as kernel learning and support vector machines. There is also a good coverage of the methods which are much less known to the community but could be very useful methods such as principal coordiante analysis, featureless pattern recognitions and multivariate pattern comparison. The usefulness of these methods were demonstrated through several real-life applications including single nucleotide polymorphism analysis, DNA microarray analysis and a few human and mice metabolomic profiling studies. The comprehensive coverage of chemometrics pattern recognition techniues combined with several applications could be very useful to those who work in a similar discipline.