Quadratic Multiform Separation: A New Classification Model in Machine Learning
It addresses classification efficiency and accuracy for machine learning practitioners, but appears incremental as it builds on existing models.
The paper introduces a new classification model that achieves comparable predictive accuracy and significantly faster runtime than common models, while also identifying a subset of unseen samples with much higher accuracy.
In this paper we present a new classification model in machine learning. Our result is threefold: 1) The model produces comparable predictive accuracy to that of most common classification models. 2) It runs significantly faster than most common classification models. 3) It has the ability to identify a portion of unseen samples for which class labels can be found with much higher predictive accuracy. Currently there are several patents pending on the proposed model.