TY - GEN

T1 - Learning of mahalanobis discriminant functions by a neural network

AU - Ito, Yoshifusa

AU - Izumi, Hiroyuki

AU - Srinivasan, Cidambi

PY - 2009

Y1 - 2009

N2 - It is known that a neural network can learn a Bayesian discriminant function. Ito et al. (2006) has pointed out that if the inner potential of the output unit of the network is shifted by a constant, the output becomes a Mahalanobis discriminant function. However, it was a heavy task for the network to calculate the constant. Here, we propose a new algorithm with which the network can estimate the constant easily. This method can be extended to higher dimensional classificasions problems without much effort.

AB - It is known that a neural network can learn a Bayesian discriminant function. Ito et al. (2006) has pointed out that if the inner potential of the output unit of the network is shifted by a constant, the output becomes a Mahalanobis discriminant function. However, it was a heavy task for the network to calculate the constant. Here, we propose a new algorithm with which the network can estimate the constant easily. This method can be extended to higher dimensional classificasions problems without much effort.

UR - http://www.scopus.com/inward/record.url?scp=76649116538&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=76649116538&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-10677-4_47

DO - 10.1007/978-3-642-10677-4_47

M3 - Conference contribution

AN - SCOPUS:76649116538

SN - 3642106765

SN - 9783642106767

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 417

EP - 424

BT - Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings

Y2 - 1 December 2009 through 5 December 2009

ER -