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» Time Series Causality Inference Using Echo State Networks
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NN
2007
Springer
162views Neural Networks» more  NN 2007»
13 years 4 months ago
Learning grammatical structure with Echo State Networks
Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. Howeve...
Matthew H. Tong, Adam D. Bickett, Eric M. Christia...
JMLR
2011
175views more  JMLR 2011»
12 years 12 months ago
Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data
This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspecti...
Alard Roebroeck, Anil K. Seth, Pedro A. Valdes-Sos...
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
13 years 9 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
BIOINFORMATICS
2002
146views more  BIOINFORMATICS 2002»
13 years 4 months ago
A duplication growth model of gene expression networks
Motivation: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expre...
Ashish Bhan, David J. Galas, T. Gregory Dewey
IJACTAICIT
2010
153views more  IJACTAICIT 2010»
12 years 11 months ago
Prediction Using Recurrent Neural Network Based Fuzzy Inference system by the Modified Bees Algorithm
In this paper, a recurrent neural network based fuzzy inference system (RNFIS) for prediction is proposed. A recurrent network is embedded in the RNFIS by adding feedback connecti...
Zahra Khanmirzaei, Mohammad Teshnehlab