Get Predictive Modular Neural Networks Applications to Time Series (The Springer International Series in Engineering and Computer Science)

Free Predictive Modular Neural Networks Applications to Time Series (The Springer International Series in Engineering and Computer Science)

Free Predictive Modular Neural Networks Applications to Time Series (The Springer International Series in Engineering and Computer Science)

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Book Details :
Published on: 1998-09-30
Released on:
Original language: English

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 of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network. Home - Springer Featured Book Consequences of Microbial Interactions with Hydrocarbons Oils and Lipids: Production of Fuels and Chemicals Published 2017 New books and journals ... International Journal of Engineering Research and ... International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research .. Deep learning in neural networks: An overview Abstract. In recent years deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. Energy models for demand forecastingA review 1. Introduction. The twentieth century witnessed a transition from coal based to petroleum based resources. With the advent of industrialization and globalization ... Speech recognition - Wikipedia Neural networks emerged as an attractive acoustic modeling approach in ASR in the late 1980s. Since then neural networks have been used in many aspects of speech ... Publications Page - Cambridge Machine Learning Group [ full BibTeX file] 2017 2016. Matej Balog Alexander L. Gaunt Marc Brockschmidt Sebastian Nowozin and Daniel Tarlow. DeepCoder: Learning to write programs. Machine Learning Group Publications - University of Cambridge Matej Balog Balaji Lakshminarayanan Zoubin Ghahramani Daniel M. Roy and Yee Whye Teh. The Mondrian kernel. In 32nd Conference on Uncertainty in Artificial ... Time Series Analysis for Business Forecasting Balancing Success in Business Without metrics management can be a nebulous if not impossible exercise. How can we tell if we have met our goals if we do not know ... Resolve a DOI Name Type or paste a DOI name into the text box. Click Go. Your browser will take you to a Web page (URL) associated with that DOI name. Send questions or comments to doi ... Research projects - Australian Institute for ... Project keywords. Nanobiotechnology Health Antibody Targeted delivery Nanoparticle Cancer Cytotoxins. Project summary. The research projects are associated with ...
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