Summary of Kernel Stochastic Configuration Networks For Nonlinear Regression, by Yongxuan Chen and Dianhui Wang
Kernel Stochastic Configuration Networks for Nonlinear Regression
by Yongxuan Chen, Dianhui Wang
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a new class of randomized learner models called kernel stochastic configuration networks (KSCNs), which aim to improve model representation learning capability and performance stability. KSCNs are based on stochastic configuration networks (SCNs) that use random parameters assignment with a supervisory mechanism, resulting in the universal approximation property at an algorithmic level. The authors propose an algorithm for constructing KSCNs by using the random bases of the SCN model to span a reproducing kernel Hilbert space (RKHS). Experimental results show that KSCNs outperform original SCNs and typical kernel methods on three benchmark datasets, including two industrial datasets, in terms of learning performance, model stability, and robustness. The proposed KSCN learner models hold the universal approximation property, making them suitable for solving nonlinear regression problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to build machine learning models called kernel stochastic configuration networks (KSCNs). They are like special computers that can learn from data and make predictions. The authors want to improve these models so they can work better and be more stable. To do this, they propose a new way of building KSCNs by using random numbers to create a space where the model can learn. They test their idea on three different datasets and show that it works better than other similar methods. |
Keywords
» Artificial intelligence » Machine learning » Regression » Representation learning
