Next-generation reservoir computing validated by classification task
This work addresses a gap in evaluating NG-RC for machine learning researchers, but it is incremental as it extends known capabilities to a new task type.
The paper tackled the lack of classification benchmarks for next-generation reservoir computing (NG-RC) by demonstrating that NG-RC performs classification tasks as effectively as conventional reservoir computing, validating its versatility in both prediction and classification.
An emerging computing paradigm, so-called next-generation reservoir computing (NG-RC) is investigated. True to its namesake, NG-RC requires no actual reservoirs for input data mixing but rather computing the polynomial terms directly from the time series inputs. However, benchmark tests so far reported have been one-sided, limited to prediction tasks of temporal waveforms such as Lorenz 63 attractor and Mackey-Glass chaotic signal. We will demonstrate for the first time that NG-RC can perform classification task as good as conventional RC. This validates the versatile computational capability of NG-RC in tasks of both prediction and classification.