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 - https://www.scopus.com/pages/publications/76649116538
UR - https://www.scopus.com/pages/publications/76649116538#tab=citedBy
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
T2 - 16th International Conference on Neural Information Processing, ICONIP 2009
Y2 - 1 December 2009 through 5 December 2009
ER -