Model selection for Extreme Minimal Learning Machine using sampling

Abstract

A combination of Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM)—to use a distance-based basis from MLM in the ridge regression like learning framework of ELM—was proposed in [8]. In the further experiments with the technique [9], it was concluded that in multilabel classification one can obtain a good validation error level without overlearning simply by using the whole training data for constructing the basis. Here, we consider possibilities to reduce the complexity of the resulting machine learning model, referred as the Extreme Minimal Leaning Machine (EMLM), by using a bidirectional sampling strategy: To sample both the feature space and the space of observations in order to identify a simpler EMLM without sacrificing its generalization performance.