Bøger i Perspectives in Neural Computing serien

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 "e;hard problems"e; 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.

A detailed formulation of neural networks from the information-theoretic viewpoint. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained.

This two-volume set contains the papers presented at ICANN 98, the 8th International Conference on Artificial Neural Networks. This meeting covered all aspects of Artificial Neural Network (ANN) related research. It includes papers from researchers in Europe, the USA and Japan.

About This Book This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs).

This volume collects together refereed versions of twenty-five papers presented at the 4th Neural Computation and Psychology Workshop, held at University College London in April 1997.

Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal.

Neural Network Applications contains the 12 papers presented at the second British Neural Network Society Meeting (NCM '91) held at King's College London on 1st October 1991. The meeting was sponsored by the Centre for Neural Networks, King's College, and the British Neural Network Society, and was also part of the DEANNA ESPRIT programme.

The papers - which have been updated where necessary to include new results - are divided into four sections, covering the foundations of neural network dynamics, oscillatory neural networks, as well as scientific and biological applications of neural networks.

This volume consists of proceedings of the one-day conference on "Coupled Oscillating Neurons" held at King's College, London on December 13th, 1990.

Subsequent tutorials on the first day covered dynamical systems and neural networks, realistic neural modelling, pattern recognition using neural networks, and a review of hardware for neural network simulations.

Sun, who respectively presented the lectures "Computation in Neuromorphic Analog VLSI Systems", "On Connectionism and Rule Extraction", "Beyond Simple Rule Extraction: Acquiring Planning Knowledge from Neural Networks" (the last two papers being part of the special session mentioned below).

As a result of this, more complex problem solutions are being attempted, whether or not the problems themselves are inherently complex.

The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years.

The theme of the 5th workshop in the series was Connectionist models in cognitive neuroscience', and the workshop aimed to bring together papers focused on the inter-relations between functional (psychological) accounts of cognition and neural accounts of underlying brain processes, linked by connectionist models.

From its early beginnings in the fifties and sixties, the field of neural networks has been steadily developing to become one of the most interdisciplinary areas of research within computer science.

From its early beginnings in the fifties and sixties, the field of neural networks has been steadily developing to become one of the most interdisciplinary areas of research within computer science.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1 . 3 . 6 1 . 4 The Plan for Neural Networks . 1 The Status of Neural Networks . 2 Reasons for the Employment of Neural Networks . 3 Neural Network Models . 4 Areas of Application . 5 Typical Applications .

Measurement consists of comparison of an unknown entity with a set of standard scales or dimensions having numerical attributes in preassigned degree. The dimensions of a complex waveform are elementary waveforms from which that waveform can be built by simple addition.

Presents neural network modelling in the areas of evolution, learning, and development. This book, organized in six sections, covers the neural basis of cognition; development and category learning; implicit learning; social cognition; and, semantics. It also covers artificial intelligence, mathematics, psychology, neurobiology, and philosophy.

Looks at how research into predicting the financial markets has progressed. This book describes the financial markets and asks whether they are indeed predictable, given the number of possible economic and financial variables. It surveys prediction models and looks at how these can be refined so as to provide the best prediction.

Following the intense research activIties of the last decade, artificial neural networks have emerged as one of the most promising new technologies for improving the quality of healthcare.

It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems.

Concepts for Neural Networks - A Survey provides a wide-ranging survey of concepts relating to the study of neural networks. It includes chapters explaining the basics of both artificial neural networks and the mathematics of neural networks, as well as chapters covering the more philosophical background to the topic and consciousness.

This volume, written by leading researchers, presents methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, where a task is decomposed into simpler problems.

Equally, there is the technical challenge of building programs that allow a machine to do something a machine has never done before. In this sense a machine that can learn can be applied in areas where little is known about possible causal relationships, and even in circumstances where causal relationships are judged not to exist.

This volume comprises a selection of papers focusing specifically on the topics of ANNs in medicine and biology. It covers three main areas: the medical applications of ANNs, such as in diagnosis and outcome prediction, medical image analysis, and medical signal processing.

Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling.