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Predictive Modular Neural Networks: Applications to Time Series (The Springer International Series in Engineering and Computer Science (466)) by Vassilios Petridis, Athanasios Kehagias (auth.) is a nonfiction available to read on EtoBox.
What is Predictive Modular Neural Networks: Applications to Time Series (The Springer International Series in Engineering and Computer Science (466)) about?
The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use
Who reads Predictive Modular Neural Networks: Applications to Time Series (The Springer International Series in Engineering and Computer Science (466))?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Vassilios Petridis, Athanasios Kehagias (auth.)
- Publisher
- Springer US
- Published
- 1998
- Language
- EN
- ISBN
- 9781461555551
- Category
- nonfiction
- Subjects
- Science, Engineering, Management
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