TY - GEN
T1 - Supervised multiple timescale recurrent neuron network model for human action classification
AU - Yu, Zhibin
AU - Mallipeddi, Rammohan
AU - Lee, Minho
PY - 2013
Y1 - 2013
N2 - Multiple time-scales recurrent neural network (MTRNN) model is a useful tool to record and regenerate a continuous signal for a dynamic task. However, the MTRNN itself cannot classify different motions because there are no output nodes for classification tasks. Therefore, in this paper, we propose a novel supervised model called supervised multiple time-scales recurrent neural network (SMTRNN) to handle the classification issue. The proposed SMTRNN can label different kinds of signals without setting the initial states. SMTRNN provided both prediction and classification signals simultaneously during testing. In addition, the experiment results show that SMTRNN successfully classifies a continuous signal including multiple kinds of actions as well predicts motions.
AB - Multiple time-scales recurrent neural network (MTRNN) model is a useful tool to record and regenerate a continuous signal for a dynamic task. However, the MTRNN itself cannot classify different motions because there are no output nodes for classification tasks. Therefore, in this paper, we propose a novel supervised model called supervised multiple time-scales recurrent neural network (SMTRNN) to handle the classification issue. The proposed SMTRNN can label different kinds of signals without setting the initial states. SMTRNN provided both prediction and classification signals simultaneously during testing. In addition, the experiment results show that SMTRNN successfully classifies a continuous signal including multiple kinds of actions as well predicts motions.
KW - Classification
KW - Continuous time-scales recurrent neuron network
KW - Human action
KW - SMTRNN
UR - https://www.scopus.com/pages/publications/84893410167
U2 - 10.1007/978-3-642-42042-9_25
DO - 10.1007/978-3-642-42042-9_25
M3 - Conference contribution
AN - SCOPUS:84893410167
SN - 9783642420412
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 203
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
Y2 - 3 November 2013 through 7 November 2013
ER -