162
Views
9
CrossRef citations to date
0
Altmetric
Original Articles

A yield forecast model for pilot products using support vector regression and manufacturing experience–the case of large-size polariser

, , &
Pages 5481-5496 | Received 29 Sep 2008, Accepted 25 May 2009, Published online: 01 Sep 2009

References

  • Amari , S and Wu , S . 1999 . Improving support vector machine classifiers by modifying kernel functions . Neural Networks , 12 ( 6 ) : 783 – 789 .
  • Anthony , M and Biggs , N . 1997 . Computational learning theory , Cambridge , , UK : Cambridge University Press .
  • Bishop , CM . 2006 . Pattern recognition and machine learning , New York : Springer .
  • Cortes , C and Vapnik , V . 1995 . Support vector networks . Machine Learning , 20 ( 3 ) : 273 – 297 .
  • Fan , A and Palaniswami , M . Selecting bankruptcy predictors using a support vector machine approach . In: Proceedings of the international joint conference on neural networks . 2000 , Como . 24–27 July . Italy, Vol. 3 (6), 354–359
  • Flake , GW and Lawrence , S . 2002 . Efficient SVM regression training with SMO . Machine Learning , 46 ( 1 ) : 271 – 290 .
  • Harville , D . 1977 . Maximum likelihood approaches to variance component estimation and to related problems . Journal of the American Statistical Association , 72 ( 358 ) : 320 – 338 .
  • Haykin , S . 1999 . “ Neural networks–a comprehensive foundation ” . Englewood Cliffs , NJ : Prentice Hall .
  • Huang , C and Moraga , C . 2004 . A diffusion-neural-network for learning from small samples . International Journal of Approximate Reasoning , 35 ( 2 ) : 137 – 161 .
  • Hirschmann , WB . 1964 . Profit from the learning curve . Harvard Business Review , 42 ( 1 ) : 105 – 119 .
  • Jennrich , RI and Schluchter , MD . 1986 . Unbalanced repeated-measures models with structured covariance matrices . Biometrics , 42 ( 4 ) : 805 – 820 .
  • Kim , KJ . 2003 . Financial time series forecasting using support vector machines . Neurocomputing , 55 ( 1 ) : 307 – 319 .
  • Laird , NM and Ware , JH . 1982 . Random-effects models for longitudinal data . Biometrics , 38 ( 4 ) : 963 – 974 .
  • Li , DC , Chen , L-S and Lin , Y-S . 2003 . Using functioanl virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environments . International Journal of Production Research , 41 ( 17 ) : 4011 – 4021 .
  • Li , DC and Lin , YS . 2006 . Using virtual sample generation to build up management knowledge in the early manufacturing stages . European Journal of Operational Research , 175 ( 1 ) : 413 – 434 .
  • Li , DC , Wu , C and Chang , FM . 2006 . Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling . International Journal of Production Research , 44 ( 21 ) : 4491 – 4509 .
  • Li , DC and Chen , CC . 2008 . “ Yield forecast of great size polarizes under learning effect ” . Thesis Tainan , , Taiwan : National Chen Kung University .
  • Min , JH and Lee , YC . 2005 . Bankruptcy prediction using support vector machine with optimal choice of kernel parameters . Expert Systems with Applications , 28 ( 4 ) : 603 – 614 .
  • Mukherjee , S , Osuna , E and Girosi , F . 1997 . Nonlinear prediction of chaotic time series using a support vector machine . Proceedings of the 1997 IEEE workshop on neural networks for signal processing VII . 24–26 September 1997 , Amelia Island , Florida . Edited by: Principe , J . pp. 511 – 520 . New York : IEEE .
  • Niyogi , P , Girosi , F and Tomaso , P . Incorporating prior information in machine learning by creating virtual examples . Proceedings of the IEEE , 86 ( 11 ) 2196 – 2209 .
  • Sánchez , AVD . 2003 . Advanced support vector machines and kernel methods . Neurocomputing , 55 ( 1 ) : 5 – 20 .
  • Schwarz , G . 1978 . Estimating the dimensions of a model . Annals of Statistics , 6 ( 2 ) : 461 – 464 .
  • Shawkat , A , Kate , A and Smith , M . 2006 . A meta-learning approach to automatic kernel selection for support vector machines . Neurocomputing , 70 ( 1 ) : 173 – 186 .
  • Smola , AJ and Scholkoph , B . 2004 . A tutorial on support vector regression . Statistic Computing , 14 ( 3 ) : 199 – 222 .
  • Tay , FEH and Cao , L . 2001 . Application of support vector machines in financial time series forecasting . Omega , 29 ( 4 ) : 309 – 317 .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.