Read Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines (Perspectives in Neural Computing) by Nikola Kasabov Online
Read ^ Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines (Perspectives in Neural Computing) PDF by ^ Nikola Kasabov eBook or Kindle ePUB Online free. Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines (Perspectives in Neural Computing) 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. 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. This book presents generic computational m
| Title | : | Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines (Perspectives in Neural Computing) |
| Author | : | |
| Rating | : | 4.77 (638 Votes) |
| Asin | : | 1852334002 |
| Format Type | : | paperback |
| Number of Pages | : | 320 Pages |
| Publish Date | : | 2017-04-06 |
| Language | : | English |
An exceptional book for computational biologists This exceptional book provides a broad overview of the methods of extracting the knowledge (or in other words building model/system/theory) from the data in various areas: from information theory and artificial intelligence to genetics. It can be very useful for biologists, who wish to use modern computational methods for analysis of microarray data, regulatory networks, cancer, analysis of clinical trials, etc.The first part (first seven chapters) of the book is devoted to the methods used in connectionists systems and here readers can find detailed description of the algorithms. In the secon. "Real-time neural network with a host of applications" according to Dr. V. Ravi. I found this book to be a landmark contribution to the state-of-the-art in neural networks pardigm. It offers some exciting neural network topologies and a distinctly new kind of thinking -'local learning' in neural networks. The author Prof. Nik Kasabov deserves to be congratulated for writing this excellent book. His explanation throughout the book is very lucid and to the point. He introduces the concept of "evolving connectionism" in a succinct way. He included a rich assortment of connectionist methods, right from the scratch, with a clear exposition of the underlying training algorithms.
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. 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. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. Most, however, are designed to deal with static processes which will not change over time. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics,
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