982.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: svm<4>Time Series Prediction 
发信站: 南京大学小百合站 (Tue Jun  4 18:43:03 2002), 站内信件

Time Series Prediction and Dynamic Resconstruction of Chaotic Systems
Dynamic reconstruction is an inverse problem that deals with reconstructing
 the dynamics of an unknown system, given a noisy time-series representing
 the evolution of one variable of the system with time. The reconstruction
 proceeds by utilizing the time-series to build a predictive model of the
 system and, then, using iterated prediction to test what the model has learned
 from the training data on the dynamics of the system. 
Reference(s): 
Predicting Time Series with Support Vector Machines. 
K.-R. Müller, A. Smola, G. R?tsch, B. Sch?lkopf, J. Kohlmorgen, V. Vapnik
. 
Proceedings ICANN'97, p.999. 
Springer Lecture Notes in Computer Science, 1997 
Nonlinear Prediction of Chaotic Time Series using a Support Vector Machine
 
S. Mukherjee, E. Osuna, and F. Girosi NNSP'97, 1997. 

Using Support Vector Machines for Time Series Prediction 
Müller, K.-R.; Smola, A.; R?tsch, G.; Sch?lkopf, B.; Kohlmorgen, J.; Vapnik
, V. 
in Advances in Kernel Methods, B. Sch?lkopf, C.J.C. Burges, and A.J. Smola
 Eds. 
Pages 242-253, MIT Press, 1999. ISBN 0-262-19416-3. 

Support Vector Machines for Dynamic Reconstruction of a Chaotic System 
Davide Matterra and Simon Haykin 
in Advances in Kernel Methods, B. Sch?lkopf, C.J.C. Burges, and A.J. Smola
 Eds. 
Pages 211-241, MIT Press, 1999. ISBN 0-262-19416-3. 

Reference link(s): 
Müller et al 
Mukherjee et al 
Data link(s): 
Synthetic data used: Mackey-Glass, Ikewda Map and Lorenz, and 
Santa Fe competition Data Set D
Entered by: Isabelle Guyon <[e-mail]> - Thursday, September 
16, 1999 at 14:54:32 (PDT) 
Comments: Müller et al report excellent performance of SVM. They set a new
 record on the Santa Fe competition data set D, 37% better than the winning
 approach during the competition. Mattera et al report that SVM are effective
 for such tasks and that their main advantage is the possibility of trading
 off the required accuracy with the number of Support Vectors
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