Advances in Independent Component Analysis

Mark Girolami

ISBN 10: 1852332638 / ISBN 13: 9781852332631

Verlag: Springer London Ltd, GB, 2000

Beschreibung

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

Bestandsnummer des Verkäufers LU-9781852332631

Bibliografische Details

• Titel: Advances in Independent Component Analysis
• Verlag: Springer London Ltd, GB
• Erscheinungsjahr: 2000
• Sprache: Englisch
• ISBN-10: 1852332638
• ISBN-13: 9781852332631
• Einband: Paperback
• Auflage: 2000 ed.
• Zustand: New

Über diesen Titel

Inhaltsangabe

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.