Evolving Connectionist Systems
Title | Evolving Connectionist Systems PDF eBook |
Author | Nikola K. Kasabov |
Publisher | Springer Science & Business Media |
Pages | 465 |
Release | 2007-08-23 |
Genre | Computers |
ISBN | 1846283477 |
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.
Evolving Connectionist Systems for On-line, Knowledge-based Learning
Title | Evolving Connectionist Systems for On-line, Knowledge-based Learning PDF eBook |
Author | Nikola K. Kasabov |
Publisher | |
Pages | 31 |
Release | 1999 |
Genre | Automatic speech recognition |
ISBN |
Evolving Connectionist Systems
Title | Evolving Connectionist Systems PDF eBook |
Author | Nikola K. Kasabov |
Publisher | Springer Science & Business Media |
Pages | 324 |
Release | 2003 |
Genre | Artificial intelligence |
ISBN | 1852334002 |
Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems.
Evolving Connectionist Systems
Title | Evolving Connectionist Systems PDF eBook |
Author | Nikola Kasabov |
Publisher | Springer Science & Business Media |
Pages | 308 |
Release | 2013-03-14 |
Genre | Computers |
ISBN | 144713740X |
Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.
Evolving Connectionist Systems
Title | Evolving Connectionist Systems PDF eBook |
Author | Nikola Kasabov |
Publisher | Springer |
Pages | 451 |
Release | 2009-10-12 |
Genre | Computers |
ISBN | 9781848004894 |
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.
Evolving Connectionist Systems
Title | Evolving Connectionist Systems PDF eBook |
Author | Nikola K. Kasabov |
Publisher | |
Pages | 322 |
Release | 2014-01-15 |
Genre | |
ISBN | 9781447137412 |
Connectionist-Symbolic Integration
Title | Connectionist-Symbolic Integration PDF eBook |
Author | Ron Sun |
Publisher | Psychology Press |
Pages | 391 |
Release | 2013-04-15 |
Genre | Psychology |
ISBN | 1134802064 |
A variety of ideas, approaches, and techniques exist -- in terms of both architecture and learning -- and this abundance seems to lead to many exciting possibilities in terms of theoretical advances and application potentials. Despite the apparent diversity, there is clearly an underlying unifying theme: architectures that bring together symbolic and connectionist models to achieve a synthesis and synergy of the two different paradigms, and the learning and knowledge acquisition methods for developing such architectures. More effort needs to be extended to exploit the possibilities and opportunities in this area. This book is the outgrowth of The IJCAI Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, held in conjunction with the fourteenth International Joint Conference on Artificial Intelligence (IJCAI '95). Featuring various presentations and discussions, this two-day workshop brought to light many new ideas, controversies, and syntheses which lead to the present volume. This book is concerned with the development, analysis, and application of hybrid connectionist-symbolic models in artificial intelligence and cognitive science. Drawing contributions from a large international group of experts, it describes and compares a variety of models in this area. The types of models discussed cover a wide range of the evolving spectrum of hybrid models, thus serving as a well-balanced progress report on the state of the art. As such, this volume provides an information clearinghouse for various proposed approaches and models that share the common belief that connectionist and symbolic models can be usefully combined and integrated, and such integration may lead to significant advances in understanding intelligence.