# A New Learning Paradigm for Stochastic Configuration Network: SCN+

arXiv: https://arxiv.org/abs/2203.07068 · HTML: https://mlsanity.com/paper/2203.07068/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 4 of 4)

- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [Learning Using Privileged Information: SVM+ and Weighted SVM](https://mlsanity.com/paper/1306.3161/) (2013)
- [A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+](https://mlsanity.com/paper/1708.08282/) (2017)
- [Two Dimensional Stochastic Configuration Networks for Image Data Analytics](https://mlsanity.com/paper/1809.02066/) (2018)

## Abstract (arXiv)

Learning using privileged information (LUPI) paradigm, which pioneered teacher-student interaction mechanism, makes the learning models use additional information in training stage. This paper is the first to propose an incremental learning algorithm with LUPI paradigm for stochastic configuration network (SCN), named SCN+. This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN. Moreover, the convergences have been studied in this paper. Finally, experimental results indicate that SCN+ indeed performs favorably.