  Reservoir computing is a recently introduced machine learning paradigm that
has been shown to be well-suited for the processing of spatiotemporal data.
Rather than training the network node connections and weights via
backpropagation in traditional recurrent neural networks, reservoirs instead
have fixed connections and weights among the `hidden layer' nodes, and
traditionally only the weights to the output layer of neurons are trained using
linear regression. We claim that for signal classification tasks one may forgo
the weight training step entirely and instead use a simple supervised
clustering method based upon principal components of norms of reservoir states.
The proposed method is mathematically analyzed and explored through numerical
experiments on real-world data. The examples demonstrate that the proposed may
outperform the traditional trained output weight approach in terms of
classification accuracy and sensitivity to reservoir parameters.
