Algorithm Description
The provided class labels for the 3 test files are derived from precomputed features.
We only focus on the training and testing of the classifier: single layer feedforward neural network (SLFN) based on a recently proposed learning algorithm: Extreme Learning Machine (ELM)[1]. The simulation includes 4 steps:
1, Model Selection: The optimal number of hidden neurons of SLFN is estimated by train and validation set approach.
2, Training Phase: The weights and biases of SLFN are estimated by ELM using training data.
3, Testing Phase: The testing output vectors are derived by the trained SLFN for each testing data.
4, Output vector fusion and 8-smple combination: An averaging filter of 80-sample window is applied to the testing output vectors and followed by Vote strategy. Vote strategy is deployed to produce the class labels for each group of 8 averaged testing output vectors every 0.5 seconds.
Reference
l
G.-B.
Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme Learning Machine: A New Learning Scheme
of Feedforward Neural Networks”, 2004
International Joint Conference on Neural Networks (IJCNN'2004), July 25-29,
2004, Budapest, Hungary.