Dragan Obradovic – författare

This book brings together the latest research achievements from various areas of signal processing and related disciplines. It aims to present knowledge extraction and information fusion supported by state-of-the-art background material. Within the book contributions presenting both novel algorithms and existing applications, especially those (but not restricted to) on-line processing of real world data are included. The areas of Knowledge Extraction and Information Fusion are naturally linked, and the subject is of major interest for modern biomedical, environmental, and industrial applications. This provides the state-of-the-art and proposes new techniques in order to combine heterogeneous information sources.

This book brings together the latest research achievements from various areas of signal processing and related disciplines. It aims to present knowledge extraction and information fusion supported by state-of-the-art background material. Within the book contributions presenting both novel algorithms and existing applications, especially those (but not restricted to) on-line processing of real world data are included. The areas of Knowledge Extraction and Information Fusion are naturally linked, and the subject is of major interest for modern biomedical, environmental, and industrial applications. This provides the state-of-the-art and proposes new techniques in order to combine heterogeneous information sources.

Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.

This book brings together the latest research achievements from various areas of signal processing and related disciplines. It aims to present knowledge extraction and information fusion supported by state-of-the-art background material. Within the book contributions presenting both novel algorithms and existing applications, especially those (but not restricted to) on-line processing of real world data are included. The areas of Knowledge Extraction and Information Fusion are naturally linked, and the subject is of major interest for modern biomedical, environmental, and industrial applications. This provides the state-of-the-art and proposes new techniques in order to combine heterogeneous information sources.

Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.

Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.