A first attempt of reservoir pruning for classification problems
Xavier Dutoit, Hendrik VAN BRUSSEL, Marnix Nuttin · Lirias · 2007
Abstract. Reservoir Computing is a new paradigm to use artificial neural networks. Despite its promising performances, it has still some drawbacks: as the reservoir is created randomly, it needs to be large enough to be able to capture all the features of the data. We propose here a method to start with a large reservoir and then reduce its size by pruning out neurons. We then apply this method on a prototypical and a real problem. Both applications show that it allows to improve the performance for a given number of neurons. 1