Discriminative learning for minimum error classification [pattern recognition]

A formulation is proposed for minimum-error classification, in which the misclassification probability is to be minimized based on a given set of training samples. A fundamental technique for designing a classifier that approaches the objective of minimum classification error in a more direct manner than traditional methods is given. The method is contrasted with several traditional classifier designs in typical experiments to demonstrate the superiority of the new learning formulation. The method can applied to other classifier structures as well. Experimental results pertaining to a speech recognition task are provided to show the effectiveness of the technique. >

[1]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[2]  I. J. Schoenberg,et al.  The Relaxation Method for Linear Inequalities , 1954, Canadian Journal of Mathematics.

[3]  J. K. Hawkins.  Self-Organizing Systems-A Review and Commentary , 1961, Proceedings of the IRE.

[4]  W. Highleyman.  Linear Decision Functions, with Application to Pattern Recognition , 1962, Proceedings of the IRE.

[5]  R. L. Kashyap,et al.  An Algorithm for Linear Inequalities and its Applications , 1965, IEEE Trans. Electron. Comput..

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

[7]  Baxter F. Womack,et al.  An Adaptive Pattern Classification System , 1966, IEEE Trans. Syst. Sci. Cybern..

[8]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Biing-Hwang Juang,et al.  On the use of bandpass liftering in speech recognition , 1987, IEEE Trans. Acoust. Speech Signal Process..

[11]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[12]  T. Kohonen,et al.  Statistical pattern recognition with neural networks: benchmarking studies , 1988, IEEE 1988 International Conference on Neural Networks.

[13]  Frank K. Soong,et al.  On the use of instantaneous and transitional spectral information in speaker recognition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[14]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[15]  Biing-Hwang Juang,et al.  Discriminative multi-layer feed-forward networks , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.

[16]  Biing-Hwang Juang,et al.  New discriminative training algorithms based on the generalized probabilistic descent method , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.