Fuzzy Support Vector Machines with Automatic Membership Setting

[1]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Linda Kaufman,et al.  Solving the quadratic programming problem arising in support vector classification , 1999 .

[4]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[5]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[6]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[7]  C.J. Harris,et al.  Classification of unbalanced data with transparent kernels , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[8]  Kok Seng Chua,et al.  Efficient computations for large least square support vector machine classifiers , 2003, Pattern Recognit. Lett..

[9]  Sheng-De Wang,et al.  Training algorithms for fuzzy support vector machines with noisy data , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[10]  E. Mayoraz,et al.  Fusion of face and speech data for person identity verification , 1999, IEEE Trans. Neural Networks.

[11]  R. Fletcher Practical Methods of Optimization , 1988 .

[12]  Glenn Fung,et al.  Knowledge-Based Support Vector Machine Classifiers , 2002, NIPS.

[13]  Bernhard Schölkopf,et al.  Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.

[14]  Alexander J. Smola,et al.  Adaptive Margin Support Vector Machines , 2000 .

[15]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[16]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[17]  Amir F. Atiya,et al.  Introduction to financial forecasting , 1996, Applied Intelligence.

[18]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[19]  Xuegong Zhang,et al.  Using class-center vectors to build support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[20]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[21]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[22]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[23]  Jason Weston Leave-One-Out Support Vector Machines , 1999, IJCAI.

[24]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[25]  Nils J. Nilsson,et al.  Learning Machines: Foundations of Trainable Pattern-Classifying Systems , 1965 .

[26]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[27]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[28]  V. Vapnik Estimation of Dependences Based on Empirical Data , 2006 .

[29]  Ramesh C. Jain,et al.  A robust backpropagation learning algorithm for function approximation , 1994, IEEE Trans. Neural Networks.

[30]  Lijuan Cao,et al.  c-ascending support vector machines for financial time series forecasting , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

[31]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[32]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[33]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[34]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[35]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[36]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[37]  J. Platt Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[38]  Xing Li,et al.  Evolving support vector machine parameters , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[39]  M. Niranjan,et al.  Sequential support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[40]  Heow Pueh Lee,et al.  Modified support vector novelty detector using training data with outliers , 2003, Pattern Recognit. Lett..

[41]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.