Item Infomation
Title: |
Orthogonal stochastic configuration networks with adaptive construction parameter for data analytics |
Authors: |
Wei, Dai Chuanfeng, Ning Shiyu, Pei |
Issue Date: |
2023 |
Publisher: |
Springer |
Abstract: |
As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram–Schmidt orthogonalization technology. |
Description: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s44244-023-00004-4 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7402 |
Appears in Collections |
OER - Công nghệ thông tin |
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