Reservoir Computing is a relatively new field of Recurrent Neural Networks in which only the output weights are re-calculated by the training process, removing the problems associated with traditional gradient descent algorithms. As the reservoir is recurrent, it can possess short term memory, but there is a trade-off between the amount of memory a reservoir can have and its nonlinear mapping capabilities. A new, custom architecture was recently proposed to overcome this by combining a reservoir with an extreme learning machine to deliver improved results. This paper extends this architecture further by introducing a ranking and pruning algorithm which removes neurons according to their significance. This provides further insight into the type of reservoir characteristics needed for a given task, and is supported by further reservoir measures of non-linearity and memory. These techniques are demonstrated on artificial and real world data.
|Title of host publication||Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010|
|Publication status||Published - 7 Oct 2010|
|Event||2010 IEEE International Workshop on Machine Learning for Signal Processing - Kittila, Finland|
Duration: 29 Aug 2010 → 1 Sep 2010
|Conference||2010 IEEE International Workshop on Machine Learning for Signal Processing|
|Period||29/08/10 → 1/09/10|